var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://api.zotero.org/groups/4723267/items?key=08YweqYIT8pOivc6EEeHdsB6&q=Ferranti&format=bibtex&sort=date&limit=1000\",\"theme\":\"default\",\"jsonp\":\"1\",\"owner\":\"Ferranti\",\"filter\":\"\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Ferrara, Italy\",\"title\":\"Active Thruster Fault Diagnosis for an Overactuated Autonomous Surface Vessel\",\"abstract\":\"As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.\",\"language\":\"en\",\"booktitle\":\"12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SafeProcess)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Tsolakis\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Reppa\"],\"firstnames\":[\"V.\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2024\",\"bibtex\":\"@inproceedings{tsolakis_active_2024,\\n\\taddress = {Ferrara, Italy},\\n\\ttitle = {Active {Thruster} {Fault} {Diagnosis} for an {Overactuated} {Autonomous} {Surface} {Vessel}},\\n\\tabstract = {As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.},\\n\\tlanguage = {en},\\n\\tbooktitle = {12th {IFAC} {Symposium} on {Fault} {Detection}, {Supervision} and {Safety} for {Technical} {Processes} ({SafeProcess})},\\n\\tauthor = {Tsolakis, A. and Ferranti, L. and Reppa, V.},\\n\\tmonth = jun,\\n\\tyear = {2024},\\n}\\n\\n\",\"author_short\":[\"Tsolakis, A.\",\"Ferranti, L.\",\"Reppa, V.\"],\"key\":\"tsolakis_active_2024\",\"id\":\"tsolakis_active_2024\",\"bibbaseid\":\"tsolakis-ferranti-reppa-activethrusterfaultdiagnosisforanoveractuatedautonomoussurfacevessel-2024\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules\",\"doi\":\"10.1109/TITS.2024.3357284\",\"abstract\":\"This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.\",\"language\":\"en\",\"journal\":\"IEEE Transactions on Intelligent Transportation Systems\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Tsolakis\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Negenborn\"],\"firstnames\":[\"R.\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Reppa\"],\"firstnames\":[\"V.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2024\",\"keywords\":\"key_collision_avoidance, key_maritime, key_motion_planning, key_mpc\",\"bibtex\":\"@article{tsolakis_model_2024,\\n\\ttitle = {Model {Predictive} {Trajectory} {Optimization} and {Control} for {Autonomous} {Surface} {Vessels} {Considering} {Traffic} {Rules}},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf link=https://ieeexplore.ieee.org/document/10433938},\\n\\tdoi = {10.1109/TITS.2024.3357284},\\n\\tabstract = {This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.},\\n\\tlanguage = {en},\\n\\tjournal = {IEEE Transactions on Intelligent Transportation Systems},\\n\\tauthor = {Tsolakis, A. and Negenborn, R. R. and Reppa, V. and Ferranti, L.},\\n\\tmonth = feb,\\n\\tyear = {2024},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_maritime, key\\\\_motion\\\\_planning, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"Tsolakis, A.\",\"Negenborn, R. R.\",\"Reppa, V.\",\"Ferranti, L.\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf\",\"urllink\":\"https://ieeexplore.ieee.org/document/10433938\",\"key\":\"tsolakis_model_2024\",\"id\":\"tsolakis_model_2024\",\"bibbaseid\":\"tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf\",\"Link\":\"https://ieeexplore.ieee.org/document/10433938\"},\"keyword\":[\"key_collision_avoidance\",\"key_maritime\",\"key_motion_planning\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":18,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Contingency Games for Multi-Agent Interaction\",\"volume\":\"9\",\"issn\":\"2377-3766, 2377-3774\",\"doi\":\"10.1109/LRA.2024.3354548\",\"abstract\":\"Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot’s actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.\",\"language\":\"en\",\"number\":\"3\",\"urldate\":\"2024-01-29\",\"journal\":\"IEEE Robotics and Automation Letters\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Peters\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bajcsy\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chiu\"],\"firstnames\":[\"C.Y.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fridovich-Keil\"],\"firstnames\":[\"D.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laine\"],\"firstnames\":[\"F.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2024\",\"pages\":\"2208–2215\",\"bibtex\":\"@article{peters_contingency_2024,\\n\\ttitle = {Contingency {Games} for {Multi}-{Agent} {Interaction}},\\n\\tvolume = {9},\\n\\tissn = {2377-3766, 2377-3774},\\n\\turl = {paper=https://arxiv.org/abs/2304.05483 website=https://lasse-peters.net/pub/contingency-games/ RA-L=https://ieeexplore.ieee.org/document/10400882/},\\n\\tdoi = {10.1109/LRA.2024.3354548},\\n\\tabstract = {Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot’s actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.},\\n\\tlanguage = {en},\\n\\tnumber = {3},\\n\\turldate = {2024-01-29},\\n\\tjournal = {IEEE Robotics and Automation Letters},\\n\\tauthor = {Peters, L. and Bajcsy, A. and Chiu, C.Y. and Fridovich-Keil, D. and Laine, F. and Ferranti, L. and Alonso-Mora, J.},\\n\\tmonth = mar,\\n\\tyear = {2024},\\n\\tpages = {2208--2215},\\n}\\n\\n\",\"author_short\":[\"Peters, L.\",\"Bajcsy, A.\",\"Chiu, C.\",\"Fridovich-Keil, D.\",\"Laine, F.\",\"Ferranti, L.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://arxiv.org/abs/2304.05483\",\"urlwebsite\":\"https://lasse-peters.net/pub/contingency-games/\",\"urlRA-L\":\"https://ieeexplore.ieee.org/document/10400882/\",\"key\":\"peters_contingency_2024\",\"id\":\"peters_contingency_2024\",\"bibbaseid\":\"peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/abs/2304.05483\",\"Website\":\"https://lasse-peters.net/pub/contingency-games/\",\"RA-L\":\"https://ieeexplore.ieee.org/document/10400882/\"},\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Battery Identification with Cubic Spline and Moving Horizon Estimation for Mobile Robots\",\"doi\":\"10.1109/TCST.2024.3380950\",\"abstract\":\"We propose a novel approach to track the state of charge (SoC) of batteries in mobile robots to improve their capabilities. The batteries’ status is critical to accomplish their mission, but limited battery life can be a challenge. Our methodology focuses on modeling and estimating the SoC of batteries through system identification and fractional-order models. These models are flexible and can adjust to transient responses, allowing for accurate estimation of battery characteristics. Specifically, we use cubic spline interpolation to obtain the open circuit voltage (OCV) and the different resistors of the battery model. To estimate the SoC, we deploy a novel approach based on the moving horizon estimation (MHE) algorithm, which is suitable for handling poor initial estimation and constraints on the battery model. We consider the constraint on the peak discharging current, which can limit the performance of mobile robots in low-battery mode. We validate our approach by applying system identification and MHE to data from a mobile robot. The results show that our method accurately estimates the SoC despite poor initial values, enabling improved performance for mobile robots.\",\"language\":\"en\",\"journal\":\"IEEE Transactions on Control Systems Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shokri\"],\"firstnames\":[\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lyons\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pequito\"],\"firstnames\":[\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"Laura\"],\"suffixes\":[]}],\"year\":\"2024\",\"bibtex\":\"@article{shokri_battery_2024,\\n\\ttitle = {Battery {Identification} with {Cubic} {Spline} and {Moving} {Horizon} {Estimation} for {Mobile} {Robots}},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf},\\n\\tdoi = {10.1109/TCST.2024.3380950},\\n\\tabstract = {We propose a novel approach to track the state of charge (SoC) of batteries in mobile robots to improve their capabilities. The batteries’ status is critical to accomplish their mission, but limited battery life can be a challenge. Our methodology focuses on modeling and estimating the SoC of batteries through system identification and fractional-order models. These models are flexible and can adjust to transient responses, allowing for accurate estimation of battery characteristics. Specifically, we use cubic spline interpolation to obtain the open circuit voltage (OCV) and the different resistors of the battery model. To estimate the SoC, we deploy a novel approach based on the moving horizon estimation (MHE) algorithm, which is suitable for handling poor initial estimation and constraints on the battery model. We consider the constraint on the peak discharging current, which can limit the performance of mobile robots in low-battery mode. We validate our approach by applying system identification and MHE to data from a mobile robot. The results show that our method accurately estimates the SoC despite poor initial values, enabling improved performance for mobile robots.},\\n\\tlanguage = {en},\\n\\tjournal = {IEEE Transactions on Control Systems Technology},\\n\\tauthor = {Shokri, M. and Lyons, L. and Pequito, S. and Ferranti, Laura},\\n\\tyear = {2024},\\n}\\n\\n\",\"author_short\":[\"Shokri, M.\",\"Lyons, L.\",\"Pequito, S.\",\"Ferranti, L.\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf\",\"key\":\"shokri_battery_2024\",\"id\":\"shokri_battery_2024\",\"bibbaseid\":\"shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf\"},\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"DART: A Compact Platform for Autonomous Driving Research\",\"abstract\":\"This paper presents the design of a research platform for autonomous driving applications, the Delft’s Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle’s models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.\",\"language\":\"en\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Lyons\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Niesten\"],\"firstnames\":[\"T.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]}],\"year\":\"2024\",\"bibtex\":\"@misc{lyons_dart_2024,\\n\\ttitle = {{DART}: {A} {Compact} {Platform} for {Autonomous} {Driving} {Research}},\\n\\turl = {paper=https://arxiv.org/pdf/2402.07602.pdf code=https://github.com/Lorenzo-Lyons/DART},\\n\\tabstract = {This paper presents the design of a research platform for autonomous driving applications, the Delft’s Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle’s models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.},\\n\\tlanguage = {en},\\n\\tauthor = {Lyons, L. and Niesten, T. and Ferranti, L.},\\n\\tyear = {2024},\\n}\\n\\n\",\"author_short\":[\"Lyons, L.\",\"Niesten, T.\",\"Ferranti, L.\"],\"urlpaper\":\"https://arxiv.org/pdf/2402.07602.pdf\",\"urlcode\":\"https://github.com/Lorenzo-Lyons/DART\",\"key\":\"lyons_dart_2024\",\"id\":\"lyons_dart_2024\",\"bibbaseid\":\"lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2402.07602.pdf\",\"Code\":\"https://github.com/Lorenzo-Lyons/DART\"},\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees\",\"abstract\":\"This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to nonnegligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots.\",\"language\":\"en\",\"urldate\":\"2023-11-02\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Boldrer\"],\"firstnames\":[\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Serra-Gomez\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lyons\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2023\",\"note\":\"arXiv:2310.19511 [cs]\",\"keywords\":\"Computer Science - Robotics, key_automotive, key_collision_avoidance, key_control, key_motion_planning, key_mpc\",\"bibtex\":\"@misc{boldrer_rule-based_2023,\\n\\ttitle = {Rule-{Based} {Lloyd} {Algorithm} for {Multi}-{Robot} {Motion} {Planning} and {Control} with {Safety} and {Convergence} {Guarantees}},\\n\\turl = {paper=http://arxiv.org/abs/2310.19511 video=https://www.youtube.com/watch?v=ZCm-KYHxNG4 code=https://github.com/manuelboldrer/RLB},\\n\\tabstract = {This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to nonnegligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots.},\\n\\tlanguage = {en},\\n\\turldate = {2023-11-02},\\n\\tauthor = {Boldrer, M. and Serra-Gomez, A. and Lyons, L. and Alonso-Mora, J. and Ferranti, L.},\\n\\tmonth = oct,\\n\\tyear = {2023},\\n\\tnote = {arXiv:2310.19511 [cs]},\\n\\tkeywords = {Computer Science - Robotics, key\\\\_automotive, key\\\\_collision\\\\_avoidance, key\\\\_control, key\\\\_motion\\\\_planning, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"Boldrer, M.\",\"Serra-Gomez, A.\",\"Lyons, L.\",\"Alonso-Mora, J.\",\"Ferranti, L.\"],\"urlpaper\":\"http://arxiv.org/abs/2310.19511\",\"urlvideo\":\"https://www.youtube.com/watch?v=ZCm-KYHxNG4\",\"urlcode\":\"https://github.com/manuelboldrer/RLB\",\"key\":\"boldrer_rule-based_2023\",\"id\":\"boldrer_rule-based_2023\",\"bibbaseid\":\"boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2310.19511\",\"Video\":\"https://www.youtube.com/watch?v=ZCm-KYHxNG4\",\"Code\":\"https://github.com/manuelboldrer/RLB\"},\"keyword\":[\"Computer Science - Robotics\",\"key_automotive\",\"key_collision_avoidance\",\"key_control\",\"key_motion_planning\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Japan\",\"title\":\"Optimization-based Fault Mitigation for Safe Automated Driving\",\"abstract\":\"With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and identification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decisionmaking approaches that a fault-affected vehicle could employ to manage any faults. Furthermore, we show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.\",\"booktitle\":\"IFAC World Congress\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Lodder, N,\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"van der Ploeg, C.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ferranti, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Silvas, E.\"],\"suffixes\":[]}],\"year\":\"2023\",\"keywords\":\"key_automotive, key_control, key_fault_tolerant, key_mpc\",\"bibtex\":\"@inproceedings{lodder_n_optimization-based_2023,\\n\\taddress = {Japan},\\n\\ttitle = {Optimization-based {Fault} {Mitigation} for {Safe} {Automated} {Driving}},\\n\\turl = {paper=https://arxiv.org/pdf/2303.18165.pdf},\\n\\tabstract = {With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and\\nidentification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decisionmaking approaches that a fault-affected vehicle could employ to manage any faults. Furthermore,\\nwe show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.},\\n\\tbooktitle = {{IFAC} {World} {Congress}},\\n\\tauthor = {{Lodder, N,} and {van der Ploeg, C.} and {Ferranti, L.} and {Silvas, E.}},\\n\\tyear = {2023},\\n\\tkeywords = {key\\\\_automotive, key\\\\_control, key\\\\_fault\\\\_tolerant, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"Lodder, N,\",\"van der Ploeg, C.\",\"Ferranti, L.\",\"Silvas, E.\"],\"urlpaper\":\"https://arxiv.org/pdf/2303.18165.pdf\",\"key\":\"lodder_n_optimization-based_2023\",\"id\":\"lodder_n_optimization-based_2023\",\"bibbaseid\":\"lodder-vanderploeg-ferranti-silvas-optimizationbasedfaultmitigationforsafeautomateddriving-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2303.18165.pdf\"},\"keyword\":[\"key_automotive\",\"key_control\",\"key_fault_tolerant\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Scenario-Based Motion Planning with Bounded Probability of Collision\",\"abstract\":\"Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans\",\"author\":[{\"propositions\":[\"de\"],\"lastnames\":[\"Groot\"],\"firstnames\":[\"O.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gavrila\"],\"firstnames\":[\"D.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"year\":\"2023\",\"keywords\":\"key_collision_avoidance, key_control, key_motion_planning, key_mpc\",\"bibtex\":\"@misc{de_groot_scenario-based_2023,\\n\\ttitle = {Scenario-{Based} {Motion} {Planning} with {Bounded} {Probability} of {Collision}},\\n\\turl = {paper=https://arxiv.org/pdf/2307.01070.pdf video=https://www.youtube.com/watch?v=-rqoTICmEO4},\\n\\tabstract = {Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans},\\n\\tauthor = {de Groot, O. and Ferranti, L. and Gavrila, D. M. and Alonso-Mora, J.},\\n\\tyear = {2023},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_control, key\\\\_motion\\\\_planning, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"de Groot, O.\",\"Ferranti, L.\",\"Gavrila, D. M.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://arxiv.org/pdf/2307.01070.pdf\",\"urlvideo\":\"https://www.youtube.com/watch?v=-rqoTICmEO4\",\"key\":\"de_groot_scenario-based_2023\",\"id\":\"de_groot_scenario-based_2023\",\"bibbaseid\":\"degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2307.01070.pdf\",\"Video\":\"https://www.youtube.com/watch?v=-rqoTICmEO4\"},\"keyword\":[\"key_collision_avoidance\",\"key_control\",\"key_motion_planning\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"London, UK\",\"title\":\"Globally Guided Trajectory Planning in Dynamic Environments\",\"abstract\":\"Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving behaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the topology information. The most suitable high-level trajectory guides a local optimization-based planner, resulting in fast and safe motion plans.We validate the proposed planner on a mobile robot in simulation and real-world experiments.\",\"booktitle\":\"2023 IEEE International Conference on Robotics and Automation (ICRA)\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"de Groot, O.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ferranti, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Gavrila, D. M.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Alonso-Mora, J.\"],\"suffixes\":[]}],\"year\":\"2023\",\"keywords\":\"key_collision_avoidance, key_motion_planning, key_mpc\",\"bibtex\":\"@inproceedings{de_groot_o_globally_2023,\\n\\taddress = {London, UK},\\n\\ttitle = {Globally {Guided} {Trajectory} {Planning} in {Dynamic} {Environments}},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf video=https://www.youtube.com/watch?v=tkRbsAuSTrA},\\n\\tabstract = {Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving\\nbehaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the\\ntopology information. The most suitable high-level trajectory\\nguides a local optimization-based planner, resulting in fast and\\nsafe motion plans.We validate the proposed planner on a mobile\\nrobot in simulation and real-world experiments.},\\n\\tbooktitle = {2023 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\\n\\tauthor = {{de Groot, O.} and {Ferranti, L.} and {Gavrila, D. M.} and {Alonso-Mora, J.}},\\n\\tyear = {2023},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_motion\\\\_planning, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"de Groot, O.\",\"Ferranti, L.\",\"Gavrila, D. M.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf\",\"urlvideo\":\"https://www.youtube.com/watch?v=tkRbsAuSTrA\",\"key\":\"de_groot_o_globally_2023\",\"id\":\"de_groot_o_globally_2023\",\"bibbaseid\":\"degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf\",\"Video\":\"https://www.youtube.com/watch?v=tkRbsAuSTrA\"},\"keyword\":[\"key_collision_avoidance\",\"key_motion_planning\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Learning Players' Objectives in Continuous Dynamic Games from Partial State Observations\",\"abstract\":\"Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.\",\"journal\":\"International Journal of Robotics Research\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Peters, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Rubies-Royo, Vicenç\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Tomlin, C. J\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ferranti, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Alonso-Mora, J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stachniss\"],\"firstnames\":[\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fridovich-Keil\"],\"firstnames\":[\"D\"],\"suffixes\":[]}],\"year\":\"2023\",\"bibtex\":\"@article{peters_l_learning_2023,\\n\\ttitle = {Learning {Players}' {Objectives} in {Continuous} {Dynamic} {Games} from {Partial} {State} {Observations}},\\n\\turl = {paper=https://arxiv.org/pdf/2302.01999.pdf},\\n\\tabstract = {Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.},\\n\\tjournal = {International Journal of Robotics Research},\\n\\tauthor = {{Peters, L.} and {Rubies-Royo, Vicenç} and {Tomlin, C. J} and {Ferranti, L.} and {Alonso-Mora, J.} and Stachniss, C. and Fridovich-Keil, D},\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Peters, L.\",\"Rubies-Royo, Vicenç\",\"Tomlin, C. J\",\"Ferranti, L.\",\"Alonso-Mora, J.\",\"Stachniss, C.\",\"Fridovich-Keil, D\"],\"urlpaper\":\"https://arxiv.org/pdf/2302.01999.pdf\",\"key\":\"peters_l_learning_2023\",\"id\":\"peters_l_learning_2023\",\"bibbaseid\":\"peters-rubiesroyo-tomlin-ferranti-alonsomora-stachniss-fridovichkeil-learningplayersobjectivesincontinuousdynamicgamesfrompartialstateobservations-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2302.01999.pdf\"},\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning\",\"abstract\":\"The up-and-coming concept of Industry 5.0 foresees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling approach to address such a problem. However, more focus should be put on finding safe solutions which optimize the cost associated with the demonstrations collection process. This paper introduces a preliminary outline of a system, namely EValueAction (EVA), designed to assist the human in the process of collecting interactive demonstrations taking advantage of simulation to safely avoid failures. A policy is pre-trained with human-demonstrations and, where needed, new informative data are interactively gathered and aggregated to iteratively improve the initial policy. A trial case study further reinforces the relevance of the work by demonstrating the crucial role of informative demonstrations for generalization.\",\"booktitle\":\"IEEE INDIN 2023\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sibona\"],\"firstnames\":[\"F.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Luijkx\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[\"van\",\"der\"],\"lastnames\":[\"Heijden\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Indri\"],\"firstnames\":[\"M.\"],\"suffixes\":[]}],\"year\":\"2023\",\"bibtex\":\"@inproceedings{sibona_evalueaction_2023,\\n\\ttitle = {{EValueAction}: a proposal for policy evaluation in simulation to support interactive imitation learning},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf},\\n\\tabstract = {The up-and-coming concept of Industry 5.0 foresees\\nhuman-centric flexible production lines, where collaborative\\nrobots support human workforce. In order to allow a seamless\\ncollaboration between intelligent robots and human workers,\\ndesigning solutions for non-expert users is crucial. Learning from\\ndemonstration emerged as the enabling approach to address such\\na problem. However, more focus should be put on finding safe\\nsolutions which optimize the cost associated with the demonstrations\\ncollection process. This paper introduces a preliminary outline\\nof a system, namely EValueAction (EVA), designed to assist\\nthe human in the process of collecting interactive demonstrations\\ntaking advantage of simulation to safely avoid failures. A policy\\nis pre-trained with human-demonstrations and, where needed,\\nnew informative data are interactively gathered and aggregated\\nto iteratively improve the initial policy. A trial case study further\\nreinforces the relevance of the work by demonstrating the crucial\\nrole of informative demonstrations for generalization.},\\n\\tbooktitle = {{IEEE} {INDIN} 2023},\\n\\tauthor = {Sibona, F. and Luijkx, J. and van der Heijden, B. and Ferranti, L. and Indri, M.},\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Sibona, F.\",\"Luijkx, J.\",\"van der Heijden, B.\",\"Ferranti, L.\",\"Indri, M.\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf\",\"key\":\"sibona_evalueaction_2023\",\"id\":\"sibona_evalueaction_2023\",\"bibbaseid\":\"sibona-luijkx-vanderheijden-ferranti-indri-evalueactionaproposalforpolicyevaluationinsimulationtosupportinteractiveimitationlearning-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf\"},\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Curvature-Aware Model Predictive Contouring Control\",\"abstract\":\"We present a novel Curvature-Aware Model Predictive Contouring Control (CA-MPCC) formulation for mobile robotics motion planning. Our method aims at generalizing the traditional contouring control formulation derived from machining to autonomous driving applications. The proposed controller is able of handling sharp curvatures in the reference path while subject to non-linear constraints, such as lane boundaries and dynamic obstacle collision avoidance. Compared to a standard MPCC formulation, our method improves the reliability of the path-following algorithm and simplifies the tuning, while preserving real-time capabilities. We validate our findings in both simulations and experiments on a scaled-down car-like robot.\",\"booktitle\":\"2023 IEEE International Conference on Robotics and Automation (ICRA)\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Lyons, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ferranti, L.\"],\"suffixes\":[]}],\"year\":\"2023\",\"keywords\":\"key_automotive, key_collision_avoidance, key_motion_planning, key_mpc\",\"bibtex\":\"@inproceedings{lyons_l_curvature-aware_2023,\\n\\ttitle = {Curvature-{Aware} {Model} {Predictive} {Contouring} {Control}},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf  video=https://www.youtube.com/watch?v=6-E3I99D2sc},\\n\\tabstract = {We present a novel Curvature-Aware Model Predictive Contouring Control (CA-MPCC) formulation for mobile\\nrobotics motion planning. Our method aims at generalizing\\nthe traditional contouring control formulation derived from\\nmachining to autonomous driving applications. The proposed\\ncontroller is able of handling sharp curvatures in the reference\\npath while subject to non-linear constraints, such as lane\\nboundaries and dynamic obstacle collision avoidance. Compared to a standard MPCC formulation, our method improves\\nthe reliability of the path-following algorithm and simplifies the\\ntuning, while preserving real-time capabilities. We validate our\\nfindings in both simulations and experiments on a scaled-down\\ncar-like robot.},\\n\\tbooktitle = {2023 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\\n\\tauthor = {{Lyons, L.} and {Ferranti, L.}},\\n\\tyear = {2023},\\n\\tkeywords = {key\\\\_automotive, key\\\\_collision\\\\_avoidance, key\\\\_motion\\\\_planning, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"Lyons, L.\",\"Ferranti, L.\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf\",\"urlvideo\":\"https://www.youtube.com/watch?v=6-E3I99D2sc\",\"key\":\"lyons_l_curvature-aware_2023\",\"id\":\"lyons_l_curvature-aware_2023\",\"bibbaseid\":\"lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf\",\"Video\":\"https://www.youtube.com/watch?v=6-E3I99D2sc\"},\"keyword\":[\"key_automotive\",\"key_collision_avoidance\",\"key_motion_planning\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":19,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning\",\"abstract\":\"Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses). However, these approaches do not consider the uncertainty of the trained policy and simply always execute actions suggested by the current policy as the most probable ones. This makes them vulnerable to domain shift and inefficient in the number of required demonstrations. We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modalities in the pick and place poses using topological analysis. PARTNR employs an adaptive, sensitivity-based, gating function that decides if additional user demonstrations are required. User demonstrations are aggregated to the dataset and used for subsequent training. In this way, the policy can adapt promptly to domain shift and it can minimize the number of required demonstrations for a well-trained policy. The adaptive threshold enables to achieve the user-acceptable level of ambiguity to execute the policy autonomously and in turn, increase the trustworthiness of our system. We demonstrate the performance of PARTNR in a table-top pick and place task.\",\"booktitle\":\"NeurIPS Workshop on Robot Learning\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Luijkx, J.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ajanovic, Z.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ferranti, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Kober, J.\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"keywords\":\"key_manipulator, key_robot_learning\",\"bibtex\":\"@inproceedings{luijkx_j_partnr_2022,\\n\\ttitle = {{PARTNR}: {Pick} and place {Ambiguity} {Resolving} by {Trustworthy} {iNteractive} {leaRning}},\\n\\turl = {paper=https://arxiv.org/pdf/2211.08304.pdf, video=https://www.youtube.com/watch?v=q8S2Ua41Lik, website=https://partnr-learn.github.io/},\\n\\tabstract = {Several recent works show impressive results in mapping language-based human\\ncommands and image scene observations to direct robot executable policies (e.g.,\\npick and place poses). However, these approaches do not consider the uncertainty\\nof the trained policy and simply always execute actions suggested by the current\\npolicy as the most probable ones. This makes them vulnerable to domain shift and\\ninefficient in the number of required demonstrations. We extend previous works\\nand present the PARTNR algorithm that can detect ambiguities in the trained policy\\nby analyzing multiple modalities in the pick and place poses using topological\\nanalysis. PARTNR employs an adaptive, sensitivity-based, gating function that\\ndecides if additional user demonstrations are required. User demonstrations are\\naggregated to the dataset and used for subsequent training. In this way, the policy\\ncan adapt promptly to domain shift and it can minimize the number of required\\ndemonstrations for a well-trained policy. The adaptive threshold enables to achieve\\nthe user-acceptable level of ambiguity to execute the policy autonomously and in\\nturn, increase the trustworthiness of our system. We demonstrate the performance\\nof PARTNR in a table-top pick and place task.},\\n\\tbooktitle = {{NeurIPS} {Workshop} on {Robot} {Learning}},\\n\\tauthor = {{Luijkx, J.} and {Ajanovic, Z.} and {Ferranti, L.} and {Kober, J.}},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n\\tkeywords = {key\\\\_manipulator, key\\\\_robot\\\\_learning},\\n}\\n\\n\",\"author_short\":[\"Luijkx, J.\",\"Ajanovic, Z.\",\"Ferranti, L.\",\"Kober, J.\"],\"urlpaper\":\"https://arxiv.org/pdf/2211.08304.pdf,\",\"urlvideo\":\"https://www.youtube.com/watch?v=q8S2Ua41Lik,\",\"urlwebsite\":\"https://partnr-learn.github.io/\",\"key\":\"luijkx_j_partnr_2022\",\"id\":\"luijkx_j_partnr_2022\",\"bibbaseid\":\"luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2211.08304.pdf,\",\"Video\":\"https://www.youtube.com/watch?v=q8S2Ua41Lik,\",\"Website\":\"https://partnr-learn.github.io/\"},\"keyword\":[\"key_manipulator\",\"key_robot_learning\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Time-inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection\",\"abstract\":\"This work proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. We analyze the resiliency of the system to perturbations or temporary failures, and we validate our approach through both simulations and experiments on real robotic platforms. In the latter, we adopt Model Predictive Contouring Control as a low level controller to minimize the tracking error while accounting for the robots’ dynamical constraints and the control inputs saturation. The results obtained in the experiments are in accordance with the simulations\",\"journal\":\"IEEE Robotics and Automation Letters\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Manuel Boldrer\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Lorenzo Lyons\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Luigi Palopoli\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Daniele Fontanelli\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Laura Ferranti\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"keywords\":\"key_automotive, key_control, key_experiments, key_mpc\",\"bibtex\":\"@article{manuel_boldrer_time-inverted_2022,\\n\\ttitle = {Time-inverted {Kuramoto} {Model} {Meets} {Lissajous} {Curves}: {Multi}-{Robot} {Persistent} {Monitoring} and {Target} {Detection}},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf video=https://www.youtube.com/watch?v=pMJR5q4jNSs},\\n\\tabstract = {This work proposes a distributed strategy to achieve\\nboth persistent monitoring and target detection in a rectangular\\nand obstacle-free environment. Each robot has to repeatedly\\nfollow a smooth trajectory and avoid collisions with other robots.\\nTo achieve this goal, we rely on the time-inverted Kuramoto\\ndynamics and the use of Lissajous curves. We analyze the\\nresiliency of the system to perturbations or temporary failures,\\nand we validate our approach through both simulations and\\nexperiments on real robotic platforms. In the latter, we adopt\\nModel Predictive Contouring Control as a low level controller to\\nminimize the tracking error while accounting for the robots’\\ndynamical constraints and the control inputs saturation. The\\nresults obtained in the experiments are in accordance with the\\nsimulations},\\n\\tjournal = {IEEE Robotics and Automation Letters},\\n\\tauthor = {{Manuel Boldrer} and {Lorenzo Lyons} and {Luigi Palopoli} and {Daniele Fontanelli} and {Laura Ferranti}},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n\\tkeywords = {key\\\\_automotive, key\\\\_control, key\\\\_experiments, key\\\\_mpc},\\n}\\n\\n\",\"author_short\":[\"Manuel Boldrer\",\"Lorenzo Lyons\",\"Luigi Palopoli\",\"Daniele Fontanelli\",\"Laura Ferranti\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf\",\"urlvideo\":\"https://www.youtube.com/watch?v=pMJR5q4jNSs\",\"key\":\"manuel_boldrer_time-inverted_2022\",\"id\":\"manuel_boldrer_time-inverted_2022\",\"bibbaseid\":\"manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf\",\"Video\":\"https://www.youtube.com/watch?v=pMJR5q4jNSs\"},\"keyword\":[\"key_automotive\",\"key_control\",\"key_experiments\",\"key_mpc\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":17,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Distributed Nonlinear Trajectory Optimization for Multi-robot Motion Planning\",\"doi\":\"10.1109/TCST.2022.3211130\",\"abstract\":\"This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an on-board computation unit to solve a local control problem and communicate with neighboring autonomous robots via a wireless network. The difference between the two proposed methods is in the way the robots exchange information to coordinate. The information exchange can occur in a (i) synchronous or (ii) asynchronous fashion. By relying on the theory of the nonconvex alternating direction method of multipliers, we show that the proposed solutions converge to a (local) solution of the centralized problem. For both algorithms, the communication exchange preserves the safety of the robots, that is, collisions with neighboring autonomous robots are prevented. The proposed approaches can be applied to various multi-robot scenarios and robot models. In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in (a) an unsupervised intersection crossing and (b) a platooning scenarios.\",\"journal\":\"IEEE Transactions on Control Systems Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Lyons, L.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Negenborn, R.R.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Keviczky, T.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Alonso-Mora, J.\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2022\",\"keywords\":\"key_automotive, key_co, key_collision_avoidance, key_motion_planning, key_mpc, key_multirobot\",\"bibtex\":\"@article{ferranti_distributed_2022,\\n\\ttitle = {Distributed {Nonlinear} {Trajectory} {Optimization} for {Multi}-robot {Motion} {Planning}},\\n\\turl = {paper=https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf video=https://www.youtube.com/watch?v=o2W2OhNf5yc},\\n\\tdoi = {10.1109/TCST.2022.3211130},\\n\\tabstract = {This work presents a method for multi-robot\\ncoordination based on a novel distributed nonlinear model\\npredictive control formulation for trajectory optimization\\nand its modified version to mitigate the effects of packet\\nlosses and delays in the communication among the robots.\\nOur algorithms consider that each robot is equipped with\\nan on-board computation unit to solve a local control\\nproblem and communicate with neighboring autonomous\\nrobots via a wireless network. The difference between the\\ntwo proposed methods is in the way the robots exchange\\ninformation to coordinate. The information exchange can\\noccur in a (i) synchronous or (ii) asynchronous fashion. By\\nrelying on the theory of the nonconvex alternating direction\\nmethod of multipliers, we show that the proposed solutions\\nconverge to a (local) solution of the centralized problem.\\nFor both algorithms, the communication exchange\\npreserves the safety of the robots, that is, collisions\\nwith neighboring autonomous robots are prevented. The\\nproposed approaches can be applied to various multi-robot\\nscenarios and robot models. In this work, we assess our\\nmethods, both in simulation and with experiments, for the\\ncoordination of a team of autonomous vehicles in (a) an\\nunsupervised intersection crossing and (b) a platooning\\nscenarios.},\\n\\tjournal = {IEEE Transactions on Control Systems Technology},\\n\\tauthor = {Ferranti, L. and {Lyons, L.} and {Negenborn, R.R.} and {Keviczky, T.} and {Alonso-Mora, J.}},\\n\\tmonth = sep,\\n\\tyear = {2022},\\n\\tkeywords = {key\\\\_automotive, key\\\\_co, key\\\\_collision\\\\_avoidance, key\\\\_motion\\\\_planning, key\\\\_mpc, key\\\\_multirobot},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Lyons, L.\",\"Negenborn, R.R.\",\"Keviczky, T.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf\",\"urlvideo\":\"https://www.youtube.com/watch?v=o2W2OhNf5yc\",\"key\":\"ferranti_distributed_2022\",\"id\":\"ferranti_distributed_2022\",\"bibbaseid\":\"ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf\",\"Video\":\"https://www.youtube.com/watch?v=o2W2OhNf5yc\"},\"keyword\":[\"key_automotive\",\"key_co\",\"key_collision_avoidance\",\"key_motion_planning\",\"key_mpc\",\"key_multirobot\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning\",\"shorttitle\":\"Where to Look Next\",\"publisher\":\"arXiv\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Lodel\"],\"firstnames\":[\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brito\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Serra-Gómez\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Babuška\"],\"firstnames\":[\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2022\",\"note\":\"Accepted for publication in the Proc. of ICRA 2022\",\"keywords\":\"key_drones, key_exploration, key_mpc, key_reinforcement_learning, type_conference\",\"bibtex\":\"@misc{lodel_where_2022,\\n\\ttitle = {Where to {Look} {Next}: {Learning} {Viewpoint} {Recommendations} for {Informative} {Trajectory} {Planning}},\\n\\tshorttitle = {Where to {Look} {Next}},\\n\\turl = {paper=http://arxiv.org/abs/2203.02381 video=https://www.youtube.com/watch?v=qxabfC9I66k},\\n\\tpublisher = {arXiv},\\n\\tauthor = {Lodel, M. and Brito, B. and Serra-Gómez, A. and Ferranti, L. and Babuška, R. and Alonso-Mora, J.},\\n\\tmonth = mar,\\n\\tyear = {2022},\\n\\tnote = {Accepted for publication in the Proc. of ICRA 2022},\\n\\tkeywords = {key\\\\_drones, key\\\\_exploration, key\\\\_mpc, key\\\\_reinforcement\\\\_learning, type\\\\_conference},\\n}\\n\\n\",\"author_short\":[\"Lodel, M.\",\"Brito, B.\",\"Serra-Gómez, A.\",\"Ferranti, L.\",\"Babuška, R.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"http://arxiv.org/abs/2203.02381\",\"urlvideo\":\"https://www.youtube.com/watch?v=qxabfC9I66k\",\"key\":\"lodel_where_2022\",\"id\":\"lodel_where_2022\",\"bibbaseid\":\"lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2203.02381\",\"Video\":\"https://www.youtube.com/watch?v=qxabfC9I66k\"},\"keyword\":[\"key_drones\",\"key_exploration\",\"key_mpc\",\"key_reinforcement_learning\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Learning Mixed Strategies in Trajectory Games\",\"abstract\":\"In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional “predict then plan” approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive “mixed” strategies. We validate our approach on a number of experiments using the pursuit-evasion game “tag.”\",\"booktitle\":\"Robotics: Science and Systems (RSS)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Peters\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fridovich-Keil\"],\"firstnames\":[\"D.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stachniss\"],\"firstnames\":[\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laine\"],\"firstnames\":[\"F.\"],\"suffixes\":[]}],\"year\":\"2022\",\"note\":\"Accepted for publication in the Proc. of RSS 2022\",\"keywords\":\"key_game_theory, key_multirobot, type_conference\",\"bibtex\":\"@inproceedings{peters_learning_2022,\\n\\ttitle = {Learning {Mixed} {Strategies} in {Trajectory} {Games}},\\n\\turl = {paper=http://arxiv.org/abs/2205.00291 video=https://www.youtube.com/watch?v=gT52cKH9pvg website=https://lasse-peters.net/pub/lifted-games/ talk=https://www.youtube.com/watch?v=gT52cKH9pvg},\\n\\tabstract = {In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional “predict then plan” approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive “mixed” strategies. We validate our approach on a number of experiments using the pursuit-evasion game “tag.”},\\n\\tbooktitle = {Robotics: {Science} and {Systems} ({RSS})},\\n\\tauthor = {Peters, L. and Fridovich-Keil, D. and Ferranti, L. and Stachniss, C. and Alonso-Mora, J. and Laine, F.},\\n\\tyear = {2022},\\n\\tnote = {Accepted for publication in the Proc. of RSS 2022},\\n\\tkeywords = {key\\\\_game\\\\_theory, key\\\\_multirobot, type\\\\_conference},\\n}\\n\\n\",\"author_short\":[\"Peters, L.\",\"Fridovich-Keil, D.\",\"Ferranti, L.\",\"Stachniss, C.\",\"Alonso-Mora, J.\",\"Laine, F.\"],\"urlpaper\":\"http://arxiv.org/abs/2205.00291\",\"urlvideo\":\"https://www.youtube.com/watch?v=gT52cKH9pvg\",\"urlwebsite\":\"https://lasse-peters.net/pub/lifted-games/\",\"urltalk\":\"https://www.youtube.com/watch?v=gT52cKH9pvg\",\"key\":\"peters_learning_2022\",\"id\":\"peters_learning_2022\",\"bibbaseid\":\"peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2205.00291\",\"Video\":\"https://www.youtube.com/watch?v=gT52cKH9pvg\",\"Website\":\"https://lasse-peters.net/pub/lifted-games/\",\"Talk\":\"https://www.youtube.com/watch?v=gT52cKH9pvg\"},\"keyword\":[\"key_game_theory\",\"key_multirobot\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Prague, Czech Republic\",\"title\":\"DeepKoCo: Efficient latent planning with a task-relevant Koopman representation\",\"doi\":\"10.1109/IROS51168.2021.9636408\",\"booktitle\":\"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[\"van\",\"der\"],\"lastnames\":[\"Heijden\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kober\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Babuska\"],\"firstnames\":[\"R.\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2021\",\"keywords\":\"key_reinforcement_learning, key_representation_learning, type_conference\",\"pages\":\"183–189\",\"bibtex\":\"@inproceedings{van_der_heijden_deepkoco_2021,\\n\\taddress = {Prague, Czech Republic},\\n\\ttitle = {{DeepKoCo}: {Efficient} latent planning with a task-relevant {Koopman} representation},\\n\\turl = {paper=https://ieeexplore.ieee.org/document/9636408/ video=https://youtu.be/YyopLDu7xIo},\\n\\tdoi = {10.1109/IROS51168.2021.9636408},\\n\\tbooktitle = {2021 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {van der Heijden, B. and Ferranti, L. and Kober, J. and Babuska, R.},\\n\\tmonth = sep,\\n\\tyear = {2021},\\n\\tkeywords = {key\\\\_reinforcement\\\\_learning, key\\\\_representation\\\\_learning, type\\\\_conference},\\n\\tpages = {183--189},\\n}\\n\\n\",\"author_short\":[\"van der Heijden, B.\",\"Ferranti, L.\",\"Kober, J.\",\"Babuska, R.\"],\"urlpaper\":\"https://ieeexplore.ieee.org/document/9636408/\",\"urlvideo\":\"https://youtu.be/YyopLDu7xIo\",\"key\":\"van_der_heijden_deepkoco_2021\",\"id\":\"van_der_heijden_deepkoco_2021\",\"bibbaseid\":\"vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/9636408/\",\"Video\":\"https://youtu.be/YyopLDu7xIo\"},\"keyword\":[\"key_reinforcement_learning\",\"key_representation_learning\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Nagoya, Japan\",\"title\":\"Active Safety System for Semi-Autonomous Teleoperated Vehicles\",\"url\":\"https://ieeexplore.ieee.org/document/9669239/\",\"doi\":\"10.1109/IVWorkshops54471.2021.9669239\",\"booktitle\":\"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saparia\"],\"firstnames\":[\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schimpe\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2021\",\"keywords\":\"key_automotive, key_mpc, key_teleoperation, type_conference\",\"pages\":\"141–147\",\"bibtex\":\"@inproceedings{saparia_active_2021,\\n\\taddress = {Nagoya, Japan},\\n\\ttitle = {Active {Safety} {System} for {Semi}-{Autonomous} {Teleoperated} {Vehicles}},\\n\\turl = {https://ieeexplore.ieee.org/document/9669239/},\\n\\tdoi = {10.1109/IVWorkshops54471.2021.9669239},\\n\\tbooktitle = {2021 {IEEE} {Intelligent} {Vehicles} {Symposium} {Workshops} ({IV} {Workshops})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Saparia, S. and Schimpe, A. and Ferranti, L.},\\n\\tmonth = jul,\\n\\tyear = {2021},\\n\\tkeywords = {key\\\\_automotive, key\\\\_mpc, key\\\\_teleoperation, type\\\\_conference},\\n\\tpages = {141--147},\\n}\\n\\n\",\"author_short\":[\"Saparia, S.\",\"Schimpe, A.\",\"Ferranti, L.\"],\"key\":\"saparia_active_2021\",\"id\":\"saparia_active_2021\",\"bibbaseid\":\"saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/9669239/\"},\"keyword\":[\"key_automotive\",\"key_mpc\",\"key_teleoperation\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments\",\"volume\":\"6\",\"doi\":\"10.1109/LRA.2021.3074866\",\"number\":\"3\",\"journal\":\"IEEE Robotics and Automation Letters\",\"author\":[{\"propositions\":[\"de\"],\"lastnames\":[\"Groot\"],\"firstnames\":[\"O.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brito\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gavrila\"],\"firstnames\":[\"D.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2021\",\"keywords\":\"key_collision_avoidance, key_motion_planning, key_mpc, key_scenario_optimization, type_journal\",\"pages\":\"5389–5396\",\"bibtex\":\"@article{de_groot_scenario-based_2021,\\n\\ttitle = {Scenario-{Based} {Trajectory} {Optimization} in {Uncertain} {Dynamic} {Environments}},\\n\\tvolume = {6},\\n\\turl = {paper=https://ieeexplore.ieee.org/document/9410362/ video=https://www.youtube.com/watch?v=YONmA4EGmtI},\\n\\tdoi = {10.1109/LRA.2021.3074866},\\n\\tnumber = {3},\\n\\tjournal = {IEEE Robotics and Automation Letters},\\n\\tauthor = {de Groot, O. and Brito, B. and Ferranti, L. and Gavrila, D. M. and Alonso-Mora, J.},\\n\\tmonth = jul,\\n\\tyear = {2021},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_motion\\\\_planning, key\\\\_mpc, key\\\\_scenario\\\\_optimization, type\\\\_journal},\\n\\tpages = {5389--5396},\\n}\\n\\n\",\"author_short\":[\"de Groot, O.\",\"Brito, B.\",\"Ferranti, L.\",\"Gavrila, D. M.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://ieeexplore.ieee.org/document/9410362/\",\"urlvideo\":\"https://www.youtube.com/watch?v=YONmA4EGmtI\",\"key\":\"de_groot_scenario-based_2021\",\"id\":\"de_groot_scenario-based_2021\",\"bibbaseid\":\"degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/9410362/\",\"Video\":\"https://www.youtube.com/watch?v=YONmA4EGmtI\"},\"keyword\":[\"key_collision_avoidance\",\"key_motion_planning\",\"key_mpc\",\"key_scenario_optimization\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Integrated nonlinear model predictive control for automated driving\",\"volume\":\"106\",\"url\":\"https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240\",\"doi\":\"10.1016/j.conengprac.2020.104654\",\"language\":\"en\",\"journal\":\"Control Engineering Practice\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chowdhri\"],\"firstnames\":[\"N.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Iribarren\"],\"firstnames\":[\"F.\",\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shyrokau\"],\"firstnames\":[\"B.\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2021\",\"note\":\"Feature Paper of Control Engineering Practice for June 2022\",\"keywords\":\"key_automotive, key_evasive, key_mpc, type_journal\",\"pages\":\"104654\",\"bibtex\":\"@article{chowdhri_integrated_2021,\\n\\ttitle = {Integrated nonlinear model predictive control for automated driving},\\n\\tvolume = {106},\\n\\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240},\\n\\tdoi = {10.1016/j.conengprac.2020.104654},\\n\\tlanguage = {en},\\n\\tjournal = {Control Engineering Practice},\\n\\tauthor = {Chowdhri, N. and Ferranti, L. and Iribarren, F. S. and Shyrokau, B.},\\n\\tmonth = jan,\\n\\tyear = {2021},\\n\\tnote = {Feature Paper of Control Engineering Practice for June 2022},\\n\\tkeywords = {key\\\\_automotive, key\\\\_evasive, key\\\\_mpc, type\\\\_journal},\\n\\tpages = {104654},\\n}\\n\\n\",\"author_short\":[\"Chowdhri, N.\",\"Ferranti, L.\",\"Iribarren, F. S.\",\"Shyrokau, B.\"],\"key\":\"chowdhri_integrated_2021\",\"id\":\"chowdhri_integrated_2021\",\"bibbaseid\":\"chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240\"},\"keyword\":[\"key_automotive\",\"key_evasive\",\"key_mpc\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"A Distributed Multi-Robot Coordination Algorithm for Navigation in Tight Environments\",\"url\":\"http://arxiv.org/abs/2006.11492\",\"publisher\":\"arXiv\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Firoozi\"],\"firstnames\":[\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"X.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nejadnik\"],\"firstnames\":[\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Borrelli\"],\"firstnames\":[\"F.\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2020\",\"note\":\"arXiv:2006.11492 [cs]\",\"keywords\":\"key_collision_avoidance, key_motion_planning, key_mpc, key_robotics, key_splitting, type_arxiv\",\"bibtex\":\"@misc{firoozi_distributed_2020,\\n\\ttitle = {A {Distributed} {Multi}-{Robot} {Coordination} {Algorithm} for {Navigation} in {Tight} {Environments}},\\n\\turl = {http://arxiv.org/abs/2006.11492},\\n\\tpublisher = {arXiv},\\n\\tauthor = {Firoozi, R. and Ferranti, L. and Zhang, X. and Nejadnik, S. and Borrelli, F.},\\n\\tmonth = jun,\\n\\tyear = {2020},\\n\\tnote = {arXiv:2006.11492 [cs]},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_motion\\\\_planning, key\\\\_mpc, key\\\\_robotics, key\\\\_splitting, type\\\\_arxiv},\\n}\\n\\n\",\"author_short\":[\"Firoozi, R.\",\"Ferranti, L.\",\"Zhang, X.\",\"Nejadnik, S.\",\"Borrelli, F.\"],\"key\":\"firoozi_distributed_2020\",\"id\":\"firoozi_distributed_2020\",\"bibbaseid\":\"firoozi-ferranti-zhang-nejadnik-borrelli-adistributedmultirobotcoordinationalgorithmfornavigationintightenvironments-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2006.11492\"},\"keyword\":[\"key_collision_avoidance\",\"key_motion_planning\",\"key_mpc\",\"key_robotics\",\"key_splitting\",\"type_arxiv\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems\",\"volume\":\"8\",\"url\":\"https://ieeexplore.ieee.org/document/8955905/\",\"doi\":\"10.1109/ACCESS.2020.2965644\",\"journal\":\"IEEE Access\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pretagostini\"],\"firstnames\":[\"F.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Berardo\"],\"firstnames\":[\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ivanov\"],\"firstnames\":[\"V.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shyrokau\"],\"firstnames\":[\"B.\"],\"suffixes\":[]}],\"year\":\"2020\",\"keywords\":\"key_automotive, key_control, key_mpc, key_review, type_journal\",\"pages\":\"10951–10970\",\"bibtex\":\"@article{pretagostini_survey_2020,\\n\\ttitle = {Survey on {Wheel} {Slip} {Control} {Design} {Strategies}, {Evaluation} and {Application} to {Antilock} {Braking} {Systems}},\\n\\tvolume = {8},\\n\\turl = {https://ieeexplore.ieee.org/document/8955905/},\\n\\tdoi = {10.1109/ACCESS.2020.2965644},\\n\\tjournal = {IEEE Access},\\n\\tauthor = {Pretagostini, F. and Ferranti, L. and Berardo, G. and Ivanov, V. and Shyrokau, B.},\\n\\tyear = {2020},\\n\\tkeywords = {key\\\\_automotive, key\\\\_control, key\\\\_mpc, key\\\\_review, type\\\\_journal},\\n\\tpages = {10951--10970},\\n}\\n\\n\",\"author_short\":[\"Pretagostini, F.\",\"Ferranti, L.\",\"Berardo, G.\",\"Ivanov, V.\",\"Shyrokau, B.\"],\"key\":\"pretagostini_survey_2020\",\"id\":\"pretagostini_survey_2020\",\"bibbaseid\":\"pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/8955905/\"},\"keyword\":[\"key_automotive\",\"key_control\",\"key_mpc\",\"key_review\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults\",\"volume\":\"29\",\"url\":\"https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063\",\"doi\":\"10.1002/rnc.4063\",\"language\":\"en\",\"number\":\"16\",\"journal\":\"International Journal of Robust and Nonlinear Control\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wan\"],\"firstnames\":[\"Y.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2019\",\"keywords\":\"key_aerospace, key_fault_tolerant, key_mpc, type_journal\",\"pages\":\"5412–5428\",\"bibtex\":\"@article{ferranti_faulttolerant_2019,\\n\\ttitle = {Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults},\\n\\tvolume = {29},\\n\\turl = {https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063},\\n\\tdoi = {10.1002/rnc.4063},\\n\\tlanguage = {en},\\n\\tnumber = {16},\\n\\tjournal = {International Journal of Robust and Nonlinear Control},\\n\\tauthor = {Ferranti, L. and Wan, Y. and Keviczky, T.},\\n\\tmonth = nov,\\n\\tyear = {2019},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_fault\\\\_tolerant, key\\\\_mpc, type\\\\_journal},\\n\\tpages = {5412--5428},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Wan, Y.\",\"Keviczky, T.\"],\"key\":\"ferranti_faulttolerant_2019\",\"id\":\"ferranti_faulttolerant_2019\",\"bibbaseid\":\"ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063\"},\"keyword\":[\"key_aerospace\",\"key_fault_tolerant\",\"key_mpc\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments\",\"volume\":\"4\",\"doi\":\"10.1109/LRA.2019.2929976\",\"number\":\"4\",\"journal\":\"IEEE Robotics and Automation Letters\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Brito\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Floor\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2019\",\"keywords\":\"key_collision_avoidance, key_motion_planning, key_mpc, key_robotics, type_journal\",\"pages\":\"4459–4466\",\"bibtex\":\"@article{brito_model_2019,\\n\\ttitle = {Model {Predictive} {Contouring} {Control} for {Collision} {Avoidance} in {Unstructured} {Dynamic} {Environments}},\\n\\tvolume = {4},\\n\\turl = {paper=https://ieeexplore.ieee.org/document/8768044/ video=https://www.youtube.com/watch?v=crGTsiiilHo},\\n\\tdoi = {10.1109/LRA.2019.2929976},\\n\\tnumber = {4},\\n\\tjournal = {IEEE Robotics and Automation Letters},\\n\\tauthor = {Brito, B. and Floor, B. and Ferranti, L. and Alonso-Mora, J.},\\n\\tmonth = oct,\\n\\tyear = {2019},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_motion\\\\_planning, key\\\\_mpc, key\\\\_robotics, type\\\\_journal},\\n\\tpages = {4459--4466},\\n}\\n\\n\",\"author_short\":[\"Brito, B.\",\"Floor, B.\",\"Ferranti, L.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://ieeexplore.ieee.org/document/8768044/\",\"urlvideo\":\"https://www.youtube.com/watch?v=crGTsiiilHo\",\"key\":\"brito_model_2019\",\"id\":\"brito_model_2019\",\"bibbaseid\":\"brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/8768044/\",\"Video\":\"https://www.youtube.com/watch?v=crGTsiiilHo\"},\"keyword\":[\"key_collision_avoidance\",\"key_motion_planning\",\"key_mpc\",\"key_robotics\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Paris, France\",\"title\":\"SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users\",\"doi\":\"10.1109/IVS.2019.8813899\",\"booktitle\":\"2019 IEEE Intelligent Vehicles Symposium (IV)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brito\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pool\"],\"firstnames\":[\"E.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zheng\"],\"firstnames\":[\"Y.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ensing\"],\"firstnames\":[\"R.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Happee\"],\"firstnames\":[\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shyrokau\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kooij\"],\"firstnames\":[\"J.\",\"F.\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gavrila\"],\"firstnames\":[\"D.\",\"M.\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2019\",\"keywords\":\"key_automotive, key_experiments, key_motion_planning, key_mpc, key_perception, type_conference\",\"pages\":\"1660–1666\",\"bibtex\":\"@inproceedings{ferranti_safevru_2019,\\n\\taddress = {Paris, France},\\n\\ttitle = {{SafeVRU}: {A} {Research} {Platform} for the {Interaction} of {Self}-{Driving} {Vehicles} with {Vulnerable} {Road} {Users}},\\n\\turl = {paper=https://ieeexplore.ieee.org/document/8813899/ demo=https://www.youtube.com/watch?v=iISdbfPsR9g},\\n\\tdoi = {10.1109/IVS.2019.8813899},\\n\\tbooktitle = {2019 {IEEE} {Intelligent} {Vehicles} {Symposium} ({IV})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Ferranti, L. and Brito, B. and Pool, E. and Zheng, Y. and Ensing, R. M. and Happee, R. and Shyrokau, B. and Kooij, J. F. P. and Alonso-Mora, J. and Gavrila, D. M.},\\n\\tmonth = jun,\\n\\tyear = {2019},\\n\\tkeywords = {key\\\\_automotive, key\\\\_experiments, key\\\\_motion\\\\_planning, key\\\\_mpc, key\\\\_perception, type\\\\_conference},\\n\\tpages = {1660--1666},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Brito, B.\",\"Pool, E.\",\"Zheng, Y.\",\"Ensing, R. M.\",\"Happee, R.\",\"Shyrokau, B.\",\"Kooij, J. F. P.\",\"Alonso-Mora, J.\",\"Gavrila, D. M.\"],\"urlpaper\":\"https://ieeexplore.ieee.org/document/8813899/\",\"urldemo\":\"https://www.youtube.com/watch?v=iISdbfPsR9g\",\"key\":\"ferranti_safevru_2019\",\"id\":\"ferranti_safevru_2019\",\"bibbaseid\":\"ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/8813899/\",\"Demo\":\"https://www.youtube.com/watch?v=iISdbfPsR9g\"},\"keyword\":[\"key_automotive\",\"key_experiments\",\"key_motion_planning\",\"key_mpc\",\"key_perception\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Montreal, QC, Canada\",\"title\":\"Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments\",\"doi\":\"10.1109/ICRA.2019.8793765\",\"booktitle\":\"2019 International Conference on Robotics and Automation (ICRA)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Zhu\"],\"firstnames\":[\"H.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Juhl\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2019\",\"note\":\"Best paper award in Multi-Robot Systems\",\"keywords\":\"key_collision_avoidance, key_formation_control, key_motion_planning, key_multirobot, type_conference\",\"pages\":\"9080–9086\",\"bibtex\":\"@inproceedings{zhu_distributed_2019,\\n\\taddress = {Montreal, QC, Canada},\\n\\ttitle = {Distributed {Multi}-{Robot} {Formation} {Splitting} and {Merging} in {Dynamic} {Environments}},\\n\\turl = {paper=https://ieeexplore.ieee.org/document/8793765/ video=https://www.youtube.com/watch?v=Kx14E98Iqng},\\n\\tdoi = {10.1109/ICRA.2019.8793765},\\n\\tbooktitle = {2019 {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Zhu, H. and Juhl, J. and Ferranti, L. and Alonso-Mora, J.},\\n\\tmonth = may,\\n\\tyear = {2019},\\n\\tnote = {Best paper award in Multi-Robot Systems},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_formation\\\\_control, key\\\\_motion\\\\_planning, key\\\\_multirobot, type\\\\_conference},\\n\\tpages = {9080--9086},\\n}\\n\\n\",\"author_short\":[\"Zhu, H.\",\"Juhl, J.\",\"Ferranti, L.\",\"Alonso-Mora, J.\"],\"urlpaper\":\"https://ieeexplore.ieee.org/document/8793765/\",\"urlvideo\":\"https://www.youtube.com/watch?v=Kx14E98Iqng\",\"key\":\"zhu_distributed_2019\",\"id\":\"zhu_distributed_2019\",\"bibbaseid\":\"zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/8793765/\",\"Video\":\"https://www.youtube.com/watch?v=Kx14E98Iqng\"},\"keyword\":[\"key_collision_avoidance\",\"key_formation_control\",\"key_motion_planning\",\"key_multirobot\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications\",\"volume\":\"64\",\"shorttitle\":\"SVR-AMA\",\"url\":\"https://ieeexplore.ieee.org/document/8392438/\",\"doi\":\"10.1109/TAC.2018.2849566\",\"number\":\"5\",\"journal\":\"IEEE Transactions on Automatic Control\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pu\"],\"firstnames\":[\"Y.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jones\"],\"firstnames\":[\"C.\",\"N.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2019\",\"keywords\":\"key_aerospace, key_mpc, key_splitting, type_journal\",\"pages\":\"1800–1815\",\"bibtex\":\"@article{ferranti_svr-ama_2019,\\n\\ttitle = {{SVR}-{AMA}: {An} {Asynchronous} {Alternating} {Minimization} {Algorithm} {With} {Variance} {Reduction} for {Model} {Predictive} {Control} {Applications}},\\n\\tvolume = {64},\\n\\tshorttitle = {{SVR}-{AMA}},\\n\\turl = {https://ieeexplore.ieee.org/document/8392438/},\\n\\tdoi = {10.1109/TAC.2018.2849566},\\n\\tnumber = {5},\\n\\tjournal = {IEEE Transactions on Automatic Control},\\n\\tauthor = {Ferranti, L. and Pu, Y. and Jones, C. N. and Keviczky, T.},\\n\\tmonth = may,\\n\\tyear = {2019},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_mpc, key\\\\_splitting, type\\\\_journal},\\n\\tpages = {1800--1815},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Pu, Y.\",\"Jones, C. N.\",\"Keviczky, T.\"],\"key\":\"ferranti_svr-ama_2019\",\"id\":\"ferranti_svr-ama_2019\",\"bibbaseid\":\"ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/8392438/\"},\"keyword\":[\"key_aerospace\",\"key_mpc\",\"key_splitting\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Limassol\",\"title\":\"Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control\",\"url\":\"https://ieeexplore.ieee.org/document/8550178/\",\"doi\":\"10.23919/ECC.2018.8550178\",\"booktitle\":\"2018 European Control Conference (ECC)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Negenborn\"],\"firstnames\":[\"R.\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alonso-Mora\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2018\",\"keywords\":\"key_collision_avoidance, key_maritime, key_motion_planning, key_multirobot, key_splitting, type_conference\",\"pages\":\"2523–2528\",\"bibtex\":\"@inproceedings{ferranti_coordination_2018,\\n\\taddress = {Limassol},\\n\\ttitle = {Coordination of {Multiple} {Vessels} {Via} {Distributed} {Nonlinear} {Model} {Predictive} {Control}},\\n\\turl = {https://ieeexplore.ieee.org/document/8550178/},\\n\\tdoi = {10.23919/ECC.2018.8550178},\\n\\tbooktitle = {2018 {European} {Control} {Conference} ({ECC})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Ferranti, L. and Negenborn, R. R. and Keviczky, T. and Alonso-Mora, J.},\\n\\tmonth = jun,\\n\\tyear = {2018},\\n\\tkeywords = {key\\\\_collision\\\\_avoidance, key\\\\_maritime, key\\\\_motion\\\\_planning, key\\\\_multirobot, key\\\\_splitting, type\\\\_conference},\\n\\tpages = {2523--2528},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Negenborn, R. R.\",\"Keviczky, T.\",\"Alonso-Mora, J.\"],\"key\":\"ferranti_coordination_2018\",\"id\":\"ferranti_coordination_2018\",\"bibbaseid\":\"ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/8550178/\"},\"keyword\":[\"key_collision_avoidance\",\"key_maritime\",\"key_motion_planning\",\"key_multirobot\",\"key_splitting\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Operator-Splitting and Gradient Methods for Real-Time Predictive Flight Control Design\",\"volume\":\"40\",\"issn\":\"0731-5090, 1533-3884\",\"url\":\"https://arc.aiaa.org/doi/10.2514/1.G000288\",\"doi\":\"10.2514/1.G000288\",\"language\":\"en\",\"number\":\"2\",\"journal\":\"Journal of Guidance, Control, and Dynamics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2017\",\"keywords\":\"key_aerospace, key_mpc, key_splitting, type_journal\",\"pages\":\"265–277\",\"bibtex\":\"@article{ferranti_operator-splitting_2017,\\n\\ttitle = {Operator-{Splitting} and {Gradient} {Methods} for {Real}-{Time} {Predictive} {Flight} {Control} {Design}},\\n\\tvolume = {40},\\n\\tissn = {0731-5090, 1533-3884},\\n\\turl = {https://arc.aiaa.org/doi/10.2514/1.G000288},\\n\\tdoi = {10.2514/1.G000288},\\n\\tlanguage = {en},\\n\\tnumber = {2},\\n\\tjournal = {Journal of Guidance, Control, and Dynamics},\\n\\tauthor = {Ferranti, L. and Keviczky, T.},\\n\\tmonth = feb,\\n\\tyear = {2017},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_mpc, key\\\\_splitting, type\\\\_journal},\\n\\tpages = {265--277},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Keviczky, T.\"],\"key\":\"ferranti_operator-splitting_2017\",\"id\":\"ferranti_operator-splitting_2017\",\"bibbaseid\":\"ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arc.aiaa.org/doi/10.2514/1.G000288\"},\"keyword\":[\"key_aerospace\",\"key_mpc\",\"key_splitting\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"title\":\"Online Optimization-Based Predictive Flight Control Using First-Order Methods\",\"url\":\"http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba\",\"school\":\"Delft University of Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]}],\"year\":\"2017\",\"keywords\":\"key_aerospace, key_dual_gradient_descent, key_mpc, key_splitting\",\"bibtex\":\"@phdthesis{ferranti_online_2017,\\n\\ttitle = {Online {Optimization}-{Based} {Predictive} {Flight} {Control} {Using} {First}-{Order} {Methods}},\\n\\turl = {http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba},\\n\\tschool = {Delft University of Technology},\\n\\tauthor = {Ferranti, L.},\\n\\tyear = {2017},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_dual\\\\_gradient\\\\_descent, key\\\\_mpc, key\\\\_splitting},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\"],\"key\":\"ferranti_online_2017\",\"id\":\"ferranti_online_2017\",\"bibbaseid\":\"ferranti-onlineoptimizationbasedpredictiveflightcontrolusingfirstordermethods-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba\"},\"keyword\":[\"key_aerospace\",\"key_dual_gradient_descent\",\"key_mpc\",\"key_splitting\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Las Vegas, NV, USA\",\"title\":\"Constrained LQR using online decomposition techniques\",\"url\":\"http://ieeexplore.ieee.org/document/7798612/\",\"doi\":\"10.1109/CDC.2016.7798612\",\"booktitle\":\"2016 IEEE 55th Conference on Decision and Control (CDC)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stathopoulos\"],\"firstnames\":[\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jones\"],\"firstnames\":[\"C.\",\"N.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2016\",\"keywords\":\"key_mpc, key_splitting, type_conference\",\"pages\":\"2339–2344\",\"bibtex\":\"@inproceedings{ferranti_constrained_2016,\\n\\taddress = {Las Vegas, NV, USA},\\n\\ttitle = {Constrained {LQR} using online decomposition techniques},\\n\\turl = {http://ieeexplore.ieee.org/document/7798612/},\\n\\tdoi = {10.1109/CDC.2016.7798612},\\n\\tbooktitle = {2016 {IEEE} 55th {Conference} on {Decision} and {Control} ({CDC})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Ferranti, L. and Stathopoulos, G. and Jones, C. N. and Keviczky, T.},\\n\\tmonth = dec,\\n\\tyear = {2016},\\n\\tkeywords = {key\\\\_mpc, key\\\\_splitting, type\\\\_conference},\\n\\tpages = {2339--2344},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Stathopoulos, G.\",\"Jones, C. N.\",\"Keviczky, T.\"],\"key\":\"ferranti_constrained_2016\",\"id\":\"ferranti_constrained_2016\",\"bibbaseid\":\"ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ieeexplore.ieee.org/document/7798612/\"},\"keyword\":[\"key_mpc\",\"key_splitting\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Las Vegas, NV, USA\",\"title\":\"Asynchronous splitting design for Model Predictive Control\",\"url\":\"http://ieeexplore.ieee.org/document/7798613/\",\"doi\":\"10.1109/CDC.2016.7798613\",\"booktitle\":\"2016 IEEE 55th Conference on Decision and Control (CDC)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pu\"],\"firstnames\":[\"Y.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jones\"],\"firstnames\":[\"C.\",\"N.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2016\",\"keywords\":\"key_aerospace, key_mpc, key_splitting, type_conference\",\"pages\":\"2345–2350\",\"bibtex\":\"@inproceedings{ferranti_asynchronous_2016,\\n\\taddress = {Las Vegas, NV, USA},\\n\\ttitle = {Asynchronous splitting design for {Model} {Predictive} {Control}},\\n\\turl = {http://ieeexplore.ieee.org/document/7798613/},\\n\\tdoi = {10.1109/CDC.2016.7798613},\\n\\tbooktitle = {2016 {IEEE} 55th {Conference} on {Decision} and {Control} ({CDC})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Ferranti, L. and Pu, Y. and Jones, C. N. and Keviczky, T.},\\n\\tmonth = dec,\\n\\tyear = {2016},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_mpc, key\\\\_splitting, type\\\\_conference},\\n\\tpages = {2345--2350},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Pu, Y.\",\"Jones, C. N.\",\"Keviczky, T.\"],\"key\":\"ferranti_asynchronous_2016\",\"id\":\"ferranti_asynchronous_2016\",\"bibbaseid\":\"ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ieeexplore.ieee.org/document/7798613/\"},\"keyword\":[\"key_aerospace\",\"key_mpc\",\"key_splitting\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Aalborg, Denmark\",\"title\":\"MPC design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods\",\"url\":\"http://ieeexplore.ieee.org/document/7810513/\",\"doi\":\"10.1109/ECC.2016.7810513\",\"booktitle\":\"2016 European Control Conference (ECC)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"Laura\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"Tamas\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2016\",\"keywords\":\"key_aerospace, key_dual_gradient_descent, key_mpc, type_conference\",\"pages\":\"1562–1567\",\"bibtex\":\"@inproceedings{ferranti_mpc_2016,\\n\\taddress = {Aalborg, Denmark},\\n\\ttitle = {{MPC} design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods},\\n\\turl = {http://ieeexplore.ieee.org/document/7810513/},\\n\\tdoi = {10.1109/ECC.2016.7810513},\\n\\tbooktitle = {2016 {European} {Control} {Conference} ({ECC})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Ferranti, Laura and Keviczky, Tamas},\\n\\tmonth = jun,\\n\\tyear = {2016},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_dual\\\\_gradient\\\\_descent, key\\\\_mpc, type\\\\_conference},\\n\\tpages = {1562--1567},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Keviczky, T.\"],\"key\":\"ferranti_mpc_2016\",\"id\":\"ferranti_mpc_2016\",\"bibbaseid\":\"ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ieeexplore.ieee.org/document/7810513/\"},\"keyword\":[\"key_aerospace\",\"key_dual_gradient_descent\",\"key_mpc\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Osaka\",\"title\":\"A parallel dual fast gradient method for MPC applications\",\"url\":\"http://ieeexplore.ieee.org/document/7402568/\",\"doi\":\"10.1109/CDC.2015.7402568\",\"booktitle\":\"2015 54th IEEE Conference on Decision and Control (CDC)\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2015\",\"keywords\":\"key_aerospace, key_control, key_mpc, key_splitting, type_conference\",\"pages\":\"2406–2413\",\"bibtex\":\"@inproceedings{ferranti_parallel_2015,\\n\\taddress = {Osaka},\\n\\ttitle = {A parallel dual fast gradient method for {MPC} applications},\\n\\turl = {http://ieeexplore.ieee.org/document/7402568/},\\n\\tdoi = {10.1109/CDC.2015.7402568},\\n\\tbooktitle = {2015 54th {IEEE} {Conference} on {Decision} and {Control} ({CDC})},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Ferranti, L. and Keviczky, T.},\\n\\tmonth = dec,\\n\\tyear = {2015},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_control, key\\\\_mpc, key\\\\_splitting, type\\\\_conference},\\n\\tpages = {2406--2413},\\n}\\n\\n\",\"author_short\":[\"Ferranti, L.\",\"Keviczky, T.\"],\"key\":\"ferranti_parallel_2015\",\"id\":\"ferranti_parallel_2015\",\"bibbaseid\":\"ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ieeexplore.ieee.org/document/7402568/\"},\"keyword\":[\"key_aerospace\",\"key_control\",\"key_mpc\",\"key_splitting\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization\",\"volume\":\"36\",\"issn\":\"01432087\",\"url\":\"https://onlinelibrary.wiley.com/doi/10.1002/oca.2121\",\"doi\":\"10.1002/oca.2121\",\"number\":\"5\",\"journal\":\"Optimal Control Applications and Methods\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Necoara\"],\"firstnames\":[\"Ion\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"Laura\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"Tamás\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2015\",\"keywords\":\"key_aerospace, key_dual_gradient_descent, key_mpc, type_journal\",\"pages\":\"648–666\",\"bibtex\":\"@article{necoara_adaptive_2015,\\n\\ttitle = {An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization},\\n\\tvolume = {36},\\n\\tissn = {01432087},\\n\\turl = {https://onlinelibrary.wiley.com/doi/10.1002/oca.2121},\\n\\tdoi = {10.1002/oca.2121},\\n\\tnumber = {5},\\n\\tjournal = {Optimal Control Applications and Methods},\\n\\tauthor = {Necoara, Ion and Ferranti, Laura and Keviczky, Tamás},\\n\\tmonth = sep,\\n\\tyear = {2015},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_dual\\\\_gradient\\\\_descent, key\\\\_mpc, type\\\\_journal},\\n\\tpages = {648--666},\\n}\\n\\n\",\"author_short\":[\"Necoara, I.\",\"Ferranti, L.\",\"Keviczky, T.\"],\"key\":\"necoara_adaptive_2015\",\"id\":\"necoara_adaptive_2015\",\"bibbaseid\":\"necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://onlinelibrary.wiley.com/doi/10.1002/oca.2121\"},\"keyword\":[\"key_aerospace\",\"key_dual_gradient_descent\",\"key_mpc\",\"type_journal\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Predictive Flight Control with Active Diagnosis and Reconfiguration for Actuator Jamming\",\"volume\":\"48\",\"url\":\"https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628\",\"doi\":\"10.1016/j.ifacol.2015.11.278\",\"language\":\"en\",\"number\":\"23\",\"journal\":\"IFAC-PapersOnLine\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wan\"],\"firstnames\":[\"Y.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keviczky\"],\"firstnames\":[\"T.\"],\"suffixes\":[]}],\"year\":\"2015\",\"keywords\":\"key_aerospace, key_fault_tolerant, key_mpc, type_conference\",\"pages\":\"166–171\",\"bibtex\":\"@article{ferranti_predictive_2015,\\n\\ttitle = {Predictive {Flight} {Control} with {Active} {Diagnosis} and {Reconfiguration} for {Actuator} {Jamming}},\\n\\tvolume = {48},\\n\\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628},\\n\\tdoi = {10.1016/j.ifacol.2015.11.278},\\n\\tlanguage = {en},\\n\\tnumber = {23},\\n\\tjournal = {IFAC-PapersOnLine},\\n\\tauthor = {Ferranti, L. and Wan, Y. and Keviczky, T.},\\n\\tyear = {2015},\\n\\tkeywords = {key\\\\_aerospace, key\\\\_fault\\\\_tolerant, key\\\\_mpc, type\\\\_conference},\\n\\tpages = {166--171},\\n}\\n\",\"author_short\":[\"Ferranti, L.\",\"Wan, Y.\",\"Keviczky, T.\"],\"key\":\"ferranti_predictive_2015\",\"id\":\"ferranti_predictive_2015\",\"bibbaseid\":\"ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628\"},\"keyword\":[\"key_aerospace\",\"key_fault_tolerant\",\"key_mpc\",\"type_conference\"],\"metadata\":{\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\"html\":\"\"}],\n      metadata: {\"owner\":\"ferranti, l\",\"authorlinks\":{\"ferranti, l\":\"https://r2clab.com/\"}},\n      ownerID: \"DpZo7fzwJm4kcaape\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"items\" href=\"data:text/bibtex;base64,@inproceedings{tsolakis_active_2024,
	address = {Ferrara, Italy},
	title = {Active {Thruster} {Fault} {Diagnosis} for an {Overactuated} {Autonomous} {Surface} {Vessel}},
	abstract = {As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.},
	language = {en},
	booktitle = {12th {IFAC} {Symposium} on {Fault} {Detection}, {Supervision} and {Safety} for {Technical} {Processes} ({SafeProcess})},
	author = {Tsolakis, A. and Ferranti, L. and Reppa, V.},
	month = jun,
	year = {2024},
}



@article{tsolakis_model_2024,
	title = {Model {Predictive} {Trajectory} {Optimization} and {Control} for {Autonomous} {Surface} {Vessels} {Considering} {Traffic} {Rules}},
	url = {paper=https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf link=https://ieeexplore.ieee.org/document/10433938},
	doi = {10.1109/TITS.2024.3357284},
	abstract = {This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.},
	language = {en},
	journal = {IEEE Transactions on Intelligent Transportation Systems},
	author = {Tsolakis, A. and Negenborn, R. R. and Reppa, V. and Ferranti, L.},
	month = feb,
	year = {2024},
	keywords = {key\_collision\_avoidance, key\_maritime, key\_motion\_planning, key\_mpc},
}



@article{peters_contingency_2024,
	title = {Contingency {Games} for {Multi}-{Agent} {Interaction}},
	volume = {9},
	issn = {2377-3766, 2377-3774},
	url = {paper=https://arxiv.org/abs/2304.05483 website=https://lasse-peters.net/pub/contingency-games/ RA-L=https://ieeexplore.ieee.org/document/10400882/},
	doi = {10.1109/LRA.2024.3354548},
	abstract = {Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot’s actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efﬁciently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.},
	language = {en},
	number = {3},
	urldate = {2024-01-29},
	journal = {IEEE Robotics and Automation Letters},
	author = {Peters, L. and Bajcsy, A. and Chiu, C.Y. and Fridovich-Keil, D. and Laine, F. and Ferranti, L. and Alonso-Mora, J.},
	month = mar,
	year = {2024},
	pages = {2208--2215},
}



@article{shokri_battery_2024,
	title = {Battery {Identification} with {Cubic} {Spline} and {Moving} {Horizon} {Estimation} for {Mobile} {Robots}},
	url = {paper=https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf},
	doi = {10.1109/TCST.2024.3380950},
	abstract = {We propose a novel approach to track the state of charge (SoC) of batteries in mobile robots to improve their capabilities. The batteries’ status is critical to accomplish their mission, but limited battery life can be a challenge. Our methodology focuses on modeling and estimating the SoC of batteries through system identification and fractional-order models. These models are flexible and can adjust to transient responses, allowing for accurate estimation of battery characteristics. Specifically, we use cubic spline interpolation to obtain the open circuit voltage (OCV) and the different resistors of the battery model. To estimate the SoC, we deploy a novel approach based on the moving horizon estimation (MHE) algorithm, which is suitable for handling poor initial estimation and constraints on the battery model. We consider the constraint on the peak discharging current, which can limit the performance of mobile robots in low-battery mode. We validate our approach by applying system identification and MHE to data from a mobile robot. The results show that our method accurately estimates the SoC despite poor initial values, enabling improved performance for mobile robots.},
	language = {en},
	journal = {IEEE Transactions on Control Systems Technology},
	author = {Shokri, M. and Lyons, L. and Pequito, S. and Ferranti, Laura},
	year = {2024},
}



@misc{lyons_dart_2024,
	title = {{DART}: {A} {Compact} {Platform} for {Autonomous} {Driving} {Research}},
	url = {paper=https://arxiv.org/pdf/2402.07602.pdf code=https://github.com/Lorenzo-Lyons/DART},
	abstract = {This paper presents the design of a research platform for autonomous driving applications, the Delft’s Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle’s models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.},
	language = {en},
	author = {Lyons, L. and Niesten, T. and Ferranti, L.},
	year = {2024},
}



@misc{boldrer_rule-based_2023,
	title = {Rule-{Based} {Lloyd} {Algorithm} for {Multi}-{Robot} {Motion} {Planning} and {Control} with {Safety} and {Convergence} {Guarantees}},
	url = {paper=http://arxiv.org/abs/2310.19511 video=https://www.youtube.com/watch?v=ZCm-KYHxNG4 code=https://github.com/manuelboldrer/RLB},
	abstract = {This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to nonnegligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots.},
	language = {en},
	urldate = {2023-11-02},
	author = {Boldrer, M. and Serra-Gomez, A. and Lyons, L. and Alonso-Mora, J. and Ferranti, L.},
	month = oct,
	year = {2023},
	note = {arXiv:2310.19511 [cs]},
	keywords = {Computer Science - Robotics, key\_automotive, key\_collision\_avoidance, key\_control, key\_motion\_planning, key\_mpc},
}



@inproceedings{lodder_n_optimization-based_2023,
	address = {Japan},
	title = {Optimization-based {Fault} {Mitigation} for {Safe} {Automated} {Driving}},
	url = {paper=https://arxiv.org/pdf/2303.18165.pdf},
	abstract = {With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and
identification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decisionmaking approaches that a fault-affected vehicle could employ to manage any faults. Furthermore,
we show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.},
	booktitle = {{IFAC} {World} {Congress}},
	author = {{Lodder, N,} and {van der Ploeg, C.} and {Ferranti, L.} and {Silvas, E.}},
	year = {2023},
	keywords = {key\_automotive, key\_control, key\_fault\_tolerant, key\_mpc},
}



@misc{de_groot_scenario-based_2023,
	title = {Scenario-{Based} {Motion} {Planning} with {Bounded} {Probability} of {Collision}},
	url = {paper=https://arxiv.org/pdf/2307.01070.pdf video=https://www.youtube.com/watch?v=-rqoTICmEO4},
	abstract = {Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans},
	author = {de Groot, O. and Ferranti, L. and Gavrila, D. M. and Alonso-Mora, J.},
	year = {2023},
	keywords = {key\_collision\_avoidance, key\_control, key\_motion\_planning, key\_mpc},
}



@inproceedings{de_groot_o_globally_2023,
	address = {London, UK},
	title = {Globally {Guided} {Trajectory} {Planning} in {Dynamic} {Environments}},
	url = {paper=https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf video=https://www.youtube.com/watch?v=tkRbsAuSTrA},
	abstract = {Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving
behaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the
topology information. The most suitable high-level trajectory
guides a local optimization-based planner, resulting in fast and
safe motion plans.We validate the proposed planner on a mobile
robot in simulation and real-world experiments.},
	booktitle = {2023 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
	author = {{de Groot, O.} and {Ferranti, L.} and {Gavrila, D. M.} and {Alonso-Mora, J.}},
	year = {2023},
	keywords = {key\_collision\_avoidance, key\_motion\_planning, key\_mpc},
}



@article{peters_l_learning_2023,
	title = {Learning {Players}' {Objectives} in {Continuous} {Dynamic} {Games} from {Partial} {State} {Observations}},
	url = {paper=https://arxiv.org/pdf/2302.01999.pdf},
	abstract = {Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.},
	journal = {International Journal of Robotics Research},
	author = {{Peters, L.} and {Rubies-Royo, Vicenç} and {Tomlin, C. J} and {Ferranti, L.} and {Alonso-Mora, J.} and Stachniss, C. and Fridovich-Keil, D},
	year = {2023},
}



@inproceedings{sibona_evalueaction_2023,
	title = {{EValueAction}: a proposal for policy evaluation in simulation to support interactive imitation learning},
	url = {paper=https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf},
	abstract = {The up-and-coming concept of Industry 5.0 foresees
human-centric flexible production lines, where collaborative
robots support human workforce. In order to allow a seamless
collaboration between intelligent robots and human workers,
designing solutions for non-expert users is crucial. Learning from
demonstration emerged as the enabling approach to address such
a problem. However, more focus should be put on finding safe
solutions which optimize the cost associated with the demonstrations
collection process. This paper introduces a preliminary outline
of a system, namely EValueAction (EVA), designed to assist
the human in the process of collecting interactive demonstrations
taking advantage of simulation to safely avoid failures. A policy
is pre-trained with human-demonstrations and, where needed,
new informative data are interactively gathered and aggregated
to iteratively improve the initial policy. A trial case study further
reinforces the relevance of the work by demonstrating the crucial
role of informative demonstrations for generalization.},
	booktitle = {{IEEE} {INDIN} 2023},
	author = {Sibona, F. and Luijkx, J. and van der Heijden, B. and Ferranti, L. and Indri, M.},
	year = {2023},
}



@inproceedings{lyons_l_curvature-aware_2023,
	title = {Curvature-{Aware} {Model} {Predictive} {Contouring} {Control}},
	url = {paper=https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf  video=https://www.youtube.com/watch?v=6-E3I99D2sc},
	abstract = {We present a novel Curvature-Aware Model Predictive Contouring Control (CA-MPCC) formulation for mobile
robotics motion planning. Our method aims at generalizing
the traditional contouring control formulation derived from
machining to autonomous driving applications. The proposed
controller is able of handling sharp curvatures in the reference
path while subject to non-linear constraints, such as lane
boundaries and dynamic obstacle collision avoidance. Compared to a standard MPCC formulation, our method improves
the reliability of the path-following algorithm and simplifies the
tuning, while preserving real-time capabilities. We validate our
findings in both simulations and experiments on a scaled-down
car-like robot.},
	booktitle = {2023 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
	author = {{Lyons, L.} and {Ferranti, L.}},
	year = {2023},
	keywords = {key\_automotive, key\_collision\_avoidance, key\_motion\_planning, key\_mpc},
}



@inproceedings{luijkx_j_partnr_2022,
	title = {{PARTNR}: {Pick} and place {Ambiguity} {Resolving} by {Trustworthy} {iNteractive} {leaRning}},
	url = {paper=https://arxiv.org/pdf/2211.08304.pdf, video=https://www.youtube.com/watch?v=q8S2Ua41Lik, website=https://partnr-learn.github.io/},
	abstract = {Several recent works show impressive results in mapping language-based human
commands and image scene observations to direct robot executable policies (e.g.,
pick and place poses). However, these approaches do not consider the uncertainty
of the trained policy and simply always execute actions suggested by the current
policy as the most probable ones. This makes them vulnerable to domain shift and
inefficient in the number of required demonstrations. We extend previous works
and present the PARTNR algorithm that can detect ambiguities in the trained policy
by analyzing multiple modalities in the pick and place poses using topological
analysis. PARTNR employs an adaptive, sensitivity-based, gating function that
decides if additional user demonstrations are required. User demonstrations are
aggregated to the dataset and used for subsequent training. In this way, the policy
can adapt promptly to domain shift and it can minimize the number of required
demonstrations for a well-trained policy. The adaptive threshold enables to achieve
the user-acceptable level of ambiguity to execute the policy autonomously and in
turn, increase the trustworthiness of our system. We demonstrate the performance
of PARTNR in a table-top pick and place task.},
	booktitle = {{NeurIPS} {Workshop} on {Robot} {Learning}},
	author = {{Luijkx, J.} and {Ajanovic, Z.} and {Ferranti, L.} and {Kober, J.}},
	month = nov,
	year = {2022},
	keywords = {key\_manipulator, key\_robot\_learning},
}



@article{manuel_boldrer_time-inverted_2022,
	title = {Time-inverted {Kuramoto} {Model} {Meets} {Lissajous} {Curves}: {Multi}-{Robot} {Persistent} {Monitoring} and {Target} {Detection}},
	url = {paper=https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf video=https://www.youtube.com/watch?v=pMJR5q4jNSs},
	abstract = {This work proposes a distributed strategy to achieve
both persistent monitoring and target detection in a rectangular
and obstacle-free environment. Each robot has to repeatedly
follow a smooth trajectory and avoid collisions with other robots.
To achieve this goal, we rely on the time-inverted Kuramoto
dynamics and the use of Lissajous curves. We analyze the
resiliency of the system to perturbations or temporary failures,
and we validate our approach through both simulations and
experiments on real robotic platforms. In the latter, we adopt
Model Predictive Contouring Control as a low level controller to
minimize the tracking error while accounting for the robots’
dynamical constraints and the control inputs saturation. The
results obtained in the experiments are in accordance with the
simulations},
	journal = {IEEE Robotics and Automation Letters},
	author = {{Manuel Boldrer} and {Lorenzo Lyons} and {Luigi Palopoli} and {Daniele Fontanelli} and {Laura Ferranti}},
	month = nov,
	year = {2022},
	keywords = {key\_automotive, key\_control, key\_experiments, key\_mpc},
}



@article{ferranti_distributed_2022,
	title = {Distributed {Nonlinear} {Trajectory} {Optimization} for {Multi}-robot {Motion} {Planning}},
	url = {paper=https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf video=https://www.youtube.com/watch?v=o2W2OhNf5yc},
	doi = {10.1109/TCST.2022.3211130},
	abstract = {This work presents a method for multi-robot
coordination based on a novel distributed nonlinear model
predictive control formulation for trajectory optimization
and its modified version to mitigate the effects of packet
losses and delays in the communication among the robots.
Our algorithms consider that each robot is equipped with
an on-board computation unit to solve a local control
problem and communicate with neighboring autonomous
robots via a wireless network. The difference between the
two proposed methods is in the way the robots exchange
information to coordinate. The information exchange can
occur in a (i) synchronous or (ii) asynchronous fashion. By
relying on the theory of the nonconvex alternating direction
method of multipliers, we show that the proposed solutions
converge to a (local) solution of the centralized problem.
For both algorithms, the communication exchange
preserves the safety of the robots, that is, collisions
with neighboring autonomous robots are prevented. The
proposed approaches can be applied to various multi-robot
scenarios and robot models. In this work, we assess our
methods, both in simulation and with experiments, for the
coordination of a team of autonomous vehicles in (a) an
unsupervised intersection crossing and (b) a platooning
scenarios.},
	journal = {IEEE Transactions on Control Systems Technology},
	author = {Ferranti, L. and {Lyons, L.} and {Negenborn, R.R.} and {Keviczky, T.} and {Alonso-Mora, J.}},
	month = sep,
	year = {2022},
	keywords = {key\_automotive, key\_co, key\_collision\_avoidance, key\_motion\_planning, key\_mpc, key\_multirobot},
}



@misc{lodel_where_2022,
	title = {Where to {Look} {Next}: {Learning} {Viewpoint} {Recommendations} for {Informative} {Trajectory} {Planning}},
	shorttitle = {Where to {Look} {Next}},
	url = {paper=http://arxiv.org/abs/2203.02381 video=https://www.youtube.com/watch?v=qxabfC9I66k},
	publisher = {arXiv},
	author = {Lodel, M. and Brito, B. and Serra-Gómez, A. and Ferranti, L. and Babuška, R. and Alonso-Mora, J.},
	month = mar,
	year = {2022},
	note = {Accepted for publication in the Proc. of ICRA 2022},
	keywords = {key\_drones, key\_exploration, key\_mpc, key\_reinforcement\_learning, type\_conference},
}



@inproceedings{peters_learning_2022,
	title = {Learning {Mixed} {Strategies} in {Trajectory} {Games}},
	url = {paper=http://arxiv.org/abs/2205.00291 video=https://www.youtube.com/watch?v=gT52cKH9pvg website=https://lasse-peters.net/pub/lifted-games/ talk=https://www.youtube.com/watch?v=gT52cKH9pvg},
	abstract = {In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional “predict then plan” approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive “mixed” strategies. We validate our approach on a number of experiments using the pursuit-evasion game “tag.”},
	booktitle = {Robotics: {Science} and {Systems} ({RSS})},
	author = {Peters, L. and Fridovich-Keil, D. and Ferranti, L. and Stachniss, C. and Alonso-Mora, J. and Laine, F.},
	year = {2022},
	note = {Accepted for publication in the Proc. of RSS 2022},
	keywords = {key\_game\_theory, key\_multirobot, type\_conference},
}



@inproceedings{van_der_heijden_deepkoco_2021,
	address = {Prague, Czech Republic},
	title = {{DeepKoCo}: {Efficient} latent planning with a task-relevant {Koopman} representation},
	url = {paper=https://ieeexplore.ieee.org/document/9636408/ video=https://youtu.be/YyopLDu7xIo},
	doi = {10.1109/IROS51168.2021.9636408},
	booktitle = {2021 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
	publisher = {IEEE},
	author = {van der Heijden, B. and Ferranti, L. and Kober, J. and Babuska, R.},
	month = sep,
	year = {2021},
	keywords = {key\_reinforcement\_learning, key\_representation\_learning, type\_conference},
	pages = {183--189},
}



@inproceedings{saparia_active_2021,
	address = {Nagoya, Japan},
	title = {Active {Safety} {System} for {Semi}-{Autonomous} {Teleoperated} {Vehicles}},
	url = {https://ieeexplore.ieee.org/document/9669239/},
	doi = {10.1109/IVWorkshops54471.2021.9669239},
	booktitle = {2021 {IEEE} {Intelligent} {Vehicles} {Symposium} {Workshops} ({IV} {Workshops})},
	publisher = {IEEE},
	author = {Saparia, S. and Schimpe, A. and Ferranti, L.},
	month = jul,
	year = {2021},
	keywords = {key\_automotive, key\_mpc, key\_teleoperation, type\_conference},
	pages = {141--147},
}



@article{de_groot_scenario-based_2021,
	title = {Scenario-{Based} {Trajectory} {Optimization} in {Uncertain} {Dynamic} {Environments}},
	volume = {6},
	url = {paper=https://ieeexplore.ieee.org/document/9410362/ video=https://www.youtube.com/watch?v=YONmA4EGmtI},
	doi = {10.1109/LRA.2021.3074866},
	number = {3},
	journal = {IEEE Robotics and Automation Letters},
	author = {de Groot, O. and Brito, B. and Ferranti, L. and Gavrila, D. M. and Alonso-Mora, J.},
	month = jul,
	year = {2021},
	keywords = {key\_collision\_avoidance, key\_motion\_planning, key\_mpc, key\_scenario\_optimization, type\_journal},
	pages = {5389--5396},
}



@article{chowdhri_integrated_2021,
	title = {Integrated nonlinear model predictive control for automated driving},
	volume = {106},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240},
	doi = {10.1016/j.conengprac.2020.104654},
	language = {en},
	journal = {Control Engineering Practice},
	author = {Chowdhri, N. and Ferranti, L. and Iribarren, F. S. and Shyrokau, B.},
	month = jan,
	year = {2021},
	note = {Feature Paper of Control Engineering Practice for June 2022},
	keywords = {key\_automotive, key\_evasive, key\_mpc, type\_journal},
	pages = {104654},
}



@misc{firoozi_distributed_2020,
	title = {A {Distributed} {Multi}-{Robot} {Coordination} {Algorithm} for {Navigation} in {Tight} {Environments}},
	url = {http://arxiv.org/abs/2006.11492},
	publisher = {arXiv},
	author = {Firoozi, R. and Ferranti, L. and Zhang, X. and Nejadnik, S. and Borrelli, F.},
	month = jun,
	year = {2020},
	note = {arXiv:2006.11492 [cs]},
	keywords = {key\_collision\_avoidance, key\_motion\_planning, key\_mpc, key\_robotics, key\_splitting, type\_arxiv},
}



@article{pretagostini_survey_2020,
	title = {Survey on {Wheel} {Slip} {Control} {Design} {Strategies}, {Evaluation} and {Application} to {Antilock} {Braking} {Systems}},
	volume = {8},
	url = {https://ieeexplore.ieee.org/document/8955905/},
	doi = {10.1109/ACCESS.2020.2965644},
	journal = {IEEE Access},
	author = {Pretagostini, F. and Ferranti, L. and Berardo, G. and Ivanov, V. and Shyrokau, B.},
	year = {2020},
	keywords = {key\_automotive, key\_control, key\_mpc, key\_review, type\_journal},
	pages = {10951--10970},
}



@article{ferranti_faulttolerant_2019,
	title = {Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults},
	volume = {29},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063},
	doi = {10.1002/rnc.4063},
	language = {en},
	number = {16},
	journal = {International Journal of Robust and Nonlinear Control},
	author = {Ferranti, L. and Wan, Y. and Keviczky, T.},
	month = nov,
	year = {2019},
	keywords = {key\_aerospace, key\_fault\_tolerant, key\_mpc, type\_journal},
	pages = {5412--5428},
}



@article{brito_model_2019,
	title = {Model {Predictive} {Contouring} {Control} for {Collision} {Avoidance} in {Unstructured} {Dynamic} {Environments}},
	volume = {4},
	url = {paper=https://ieeexplore.ieee.org/document/8768044/ video=https://www.youtube.com/watch?v=crGTsiiilHo},
	doi = {10.1109/LRA.2019.2929976},
	number = {4},
	journal = {IEEE Robotics and Automation Letters},
	author = {Brito, B. and Floor, B. and Ferranti, L. and Alonso-Mora, J.},
	month = oct,
	year = {2019},
	keywords = {key\_collision\_avoidance, key\_motion\_planning, key\_mpc, key\_robotics, type\_journal},
	pages = {4459--4466},
}



@inproceedings{ferranti_safevru_2019,
	address = {Paris, France},
	title = {{SafeVRU}: {A} {Research} {Platform} for the {Interaction} of {Self}-{Driving} {Vehicles} with {Vulnerable} {Road} {Users}},
	url = {paper=https://ieeexplore.ieee.org/document/8813899/ demo=https://www.youtube.com/watch?v=iISdbfPsR9g},
	doi = {10.1109/IVS.2019.8813899},
	booktitle = {2019 {IEEE} {Intelligent} {Vehicles} {Symposium} ({IV})},
	publisher = {IEEE},
	author = {Ferranti, L. and Brito, B. and Pool, E. and Zheng, Y. and Ensing, R. M. and Happee, R. and Shyrokau, B. and Kooij, J. F. P. and Alonso-Mora, J. and Gavrila, D. M.},
	month = jun,
	year = {2019},
	keywords = {key\_automotive, key\_experiments, key\_motion\_planning, key\_mpc, key\_perception, type\_conference},
	pages = {1660--1666},
}



@inproceedings{zhu_distributed_2019,
	address = {Montreal, QC, Canada},
	title = {Distributed {Multi}-{Robot} {Formation} {Splitting} and {Merging} in {Dynamic} {Environments}},
	url = {paper=https://ieeexplore.ieee.org/document/8793765/ video=https://www.youtube.com/watch?v=Kx14E98Iqng},
	doi = {10.1109/ICRA.2019.8793765},
	booktitle = {2019 {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
	publisher = {IEEE},
	author = {Zhu, H. and Juhl, J. and Ferranti, L. and Alonso-Mora, J.},
	month = may,
	year = {2019},
	note = {Best paper award in Multi-Robot Systems},
	keywords = {key\_collision\_avoidance, key\_formation\_control, key\_motion\_planning, key\_multirobot, type\_conference},
	pages = {9080--9086},
}



@article{ferranti_svr-ama_2019,
	title = {{SVR}-{AMA}: {An} {Asynchronous} {Alternating} {Minimization} {Algorithm} {With} {Variance} {Reduction} for {Model} {Predictive} {Control} {Applications}},
	volume = {64},
	shorttitle = {{SVR}-{AMA}},
	url = {https://ieeexplore.ieee.org/document/8392438/},
	doi = {10.1109/TAC.2018.2849566},
	number = {5},
	journal = {IEEE Transactions on Automatic Control},
	author = {Ferranti, L. and Pu, Y. and Jones, C. N. and Keviczky, T.},
	month = may,
	year = {2019},
	keywords = {key\_aerospace, key\_mpc, key\_splitting, type\_journal},
	pages = {1800--1815},
}



@inproceedings{ferranti_coordination_2018,
	address = {Limassol},
	title = {Coordination of {Multiple} {Vessels} {Via} {Distributed} {Nonlinear} {Model} {Predictive} {Control}},
	url = {https://ieeexplore.ieee.org/document/8550178/},
	doi = {10.23919/ECC.2018.8550178},
	booktitle = {2018 {European} {Control} {Conference} ({ECC})},
	publisher = {IEEE},
	author = {Ferranti, L. and Negenborn, R. R. and Keviczky, T. and Alonso-Mora, J.},
	month = jun,
	year = {2018},
	keywords = {key\_collision\_avoidance, key\_maritime, key\_motion\_planning, key\_multirobot, key\_splitting, type\_conference},
	pages = {2523--2528},
}



@article{ferranti_operator-splitting_2017,
	title = {Operator-{Splitting} and {Gradient} {Methods} for {Real}-{Time} {Predictive} {Flight} {Control} {Design}},
	volume = {40},
	issn = {0731-5090, 1533-3884},
	url = {https://arc.aiaa.org/doi/10.2514/1.G000288},
	doi = {10.2514/1.G000288},
	language = {en},
	number = {2},
	journal = {Journal of Guidance, Control, and Dynamics},
	author = {Ferranti, L. and Keviczky, T.},
	month = feb,
	year = {2017},
	keywords = {key\_aerospace, key\_mpc, key\_splitting, type\_journal},
	pages = {265--277},
}



@phdthesis{ferranti_online_2017,
	title = {Online {Optimization}-{Based} {Predictive} {Flight} {Control} {Using} {First}-{Order} {Methods}},
	url = {http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba},
	school = {Delft University of Technology},
	author = {Ferranti, L.},
	year = {2017},
	keywords = {key\_aerospace, key\_dual\_gradient\_descent, key\_mpc, key\_splitting},
}



@inproceedings{ferranti_constrained_2016,
	address = {Las Vegas, NV, USA},
	title = {Constrained {LQR} using online decomposition techniques},
	url = {http://ieeexplore.ieee.org/document/7798612/},
	doi = {10.1109/CDC.2016.7798612},
	booktitle = {2016 {IEEE} 55th {Conference} on {Decision} and {Control} ({CDC})},
	publisher = {IEEE},
	author = {Ferranti, L. and Stathopoulos, G. and Jones, C. N. and Keviczky, T.},
	month = dec,
	year = {2016},
	keywords = {key\_mpc, key\_splitting, type\_conference},
	pages = {2339--2344},
}



@inproceedings{ferranti_asynchronous_2016,
	address = {Las Vegas, NV, USA},
	title = {Asynchronous splitting design for {Model} {Predictive} {Control}},
	url = {http://ieeexplore.ieee.org/document/7798613/},
	doi = {10.1109/CDC.2016.7798613},
	booktitle = {2016 {IEEE} 55th {Conference} on {Decision} and {Control} ({CDC})},
	publisher = {IEEE},
	author = {Ferranti, L. and Pu, Y. and Jones, C. N. and Keviczky, T.},
	month = dec,
	year = {2016},
	keywords = {key\_aerospace, key\_mpc, key\_splitting, type\_conference},
	pages = {2345--2350},
}



@inproceedings{ferranti_mpc_2016,
	address = {Aalborg, Denmark},
	title = {{MPC} design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods},
	url = {http://ieeexplore.ieee.org/document/7810513/},
	doi = {10.1109/ECC.2016.7810513},
	booktitle = {2016 {European} {Control} {Conference} ({ECC})},
	publisher = {IEEE},
	author = {Ferranti, Laura and Keviczky, Tamas},
	month = jun,
	year = {2016},
	keywords = {key\_aerospace, key\_dual\_gradient\_descent, key\_mpc, type\_conference},
	pages = {1562--1567},
}



@inproceedings{ferranti_parallel_2015,
	address = {Osaka},
	title = {A parallel dual fast gradient method for {MPC} applications},
	url = {http://ieeexplore.ieee.org/document/7402568/},
	doi = {10.1109/CDC.2015.7402568},
	booktitle = {2015 54th {IEEE} {Conference} on {Decision} and {Control} ({CDC})},
	publisher = {IEEE},
	author = {Ferranti, L. and Keviczky, T.},
	month = dec,
	year = {2015},
	keywords = {key\_aerospace, key\_control, key\_mpc, key\_splitting, type\_conference},
	pages = {2406--2413},
}



@article{necoara_adaptive_2015,
	title = {An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization},
	volume = {36},
	issn = {01432087},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/oca.2121},
	doi = {10.1002/oca.2121},
	number = {5},
	journal = {Optimal Control Applications and Methods},
	author = {Necoara, Ion and Ferranti, Laura and Keviczky, Tamás},
	month = sep,
	year = {2015},
	keywords = {key\_aerospace, key\_dual\_gradient\_descent, key\_mpc, type\_journal},
	pages = {648--666},
}



@article{ferranti_predictive_2015,
	title = {Predictive {Flight} {Control} with {Active} {Diagnosis} and {Reconfiguration} for {Actuator} {Jamming}},
	volume = {48},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628},
	doi = {10.1016/j.ifacol.2015.11.278},
	language = {en},
	number = {23},
	journal = {IFAC-PapersOnLine},
	author = {Ferranti, L. and Wan, Y. and Keviczky, T.},
	year = {2015},
	keywords = {key\_aerospace, key\_fault\_tolerant, key\_mpc, type\_conference},
	pages = {166--171},
}
\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=https://api.zotero.org/groups/4723267/items?key=08YweqYIT8pOivc6EEeHdsB6&amp;q=Ferranti&amp;format=bibtex&amp;sort=date&amp;limit=1000\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=https://api.zotero.org/groups/4723267/items?key=08YweqYIT8pOivc6EEeHdsB6&amp;q=Ferranti&amp;format=bibtex&amp;sort=date&amp;limit=1000\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fgroups%2F4723267%2Fitems%3Fkey%3D08YweqYIT8pOivc6EEeHdsB6%26q%3DFerranti%26format%3Dbibtex%26sort%3Ddate%26limit%3D1000&amp;theme=default&amp;jsonp=1&amp;owner=Ferranti&amp;filter=&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fgroups%2F4723267%2Fitems%3Fkey%3D08YweqYIT8pOivc6EEeHdsB6%26q%3DFerranti%26format%3Dbibtex%26sort%3Ddate%26limit%3D1000&amp;theme=default&amp;jsonp=1&amp;owner=Ferranti&amp;filter=\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fgroups%2F4723267%2Fitems%3Fkey%3D08YweqYIT8pOivc6EEeHdsB6%26q%3DFerranti%26format%3Dbibtex%26sort%3Ddate%26limit%3D1000&amp;theme=default&amp;jsonp=1&amp;owner=Ferranti&amp;filter=\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2024\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2024')\"\n      onkeypress=\"toggleGroup('group_2024')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2024</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"tsolakis-ferranti-reppa-activethrusterfaultdiagnosisforanoveractuatedautonomoussurfacevessel-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Active Thruster Fault Diagnosis for an Overactuated Autonomous Surface Vessel.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tsolakis, A.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Reppa, V.\n</span>\n\n  \n\n</span>\n\n  In <i>12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SafeProcess)</i>, Ferrara, Italy, June 2024. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsolakis-ferranti-reppa-activethrusterfaultdiagnosisforanoveractuatedautonomoussurfacevessel-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsolakis-ferranti-reppa-activethrusterfaultdiagnosisforanoveractuatedautonomoussurfacevessel-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{tsolakis_active_2024,\n\taddress = {Ferrara, Italy},\n\ttitle = {Active {Thruster} {Fault} {Diagnosis} for an {Overactuated} {Autonomous} {Surface} {Vessel}},\n\tabstract = {As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.},\n\tlanguage = {en},\n\tbooktitle = {12th {IFAC} {Symposium} on {Fault} {Detection}, {Supervision} and {Safety} for {Technical} {Processes} ({SafeProcess})},\n\tauthor = {Tsolakis, A. and Ferranti, L. and Reppa, V.},\n\tmonth = jun,\n\tyear = {2024},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024', 'https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf', event);\">\n    Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tsolakis, A.; Negenborn, R. R.; Reppa, V.;  and <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Intelligent Transportation Systems</i>. February 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf\"\n     onclick=\"log_download('tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024', 'https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://ieeexplore.ieee.org/document/10433938\"\n     onclick=\"log_download('tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024', 'https://ieeexplore.ieee.org/document/10433938', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules [link]\"\n       title=\"Link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Link</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TITS.2024.3357284\"\n     onclick=\"log_download('tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024', 'http://doi.org/10.1109/TITS.2024.3357284', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>18 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsolakis-negenborn-reppa-ferranti-modelpredictivetrajectoryoptimizationandcontrolforautonomoussurfacevesselsconsideringtrafficrules-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{tsolakis_model_2024,\n\ttitle = {Model {Predictive} {Trajectory} {Optimization} and {Control} for {Autonomous} {Surface} {Vessels} {Considering} {Traffic} {Rules}},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2024/01/Tsolakis_TITS_2024.pdf link=https://ieeexplore.ieee.org/document/10433938},\n\tdoi = {10.1109/TITS.2024.3357284},\n\tabstract = {This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.},\n\tlanguage = {en},\n\tjournal = {IEEE Transactions on Intelligent Transportation Systems},\n\tauthor = {Tsolakis, A. and Negenborn, R. R. and Reppa, V. and Ferranti, L.},\n\tmonth = feb,\n\tyear = {2024},\n\tkeywords = {key\\_collision\\_avoidance, key\\_maritime, key\\_motion\\_planning, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2304.05483\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024', 'https://arxiv.org/abs/2304.05483', event);\">\n    Contingency Games for Multi-Agent Interaction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Peters, L.; Bajcsy, A.; Chiu, C.; Fridovich-Keil, D.; Laine, F.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Robotics and Automation Letters</i>, 9(3): 2208–2215. March 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2304.05483\"\n     onclick=\"log_download('peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024', 'https://arxiv.org/abs/2304.05483', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Contingency Games for Multi-Agent Interaction [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://lasse-peters.net/pub/contingency-games/\"\n     onclick=\"log_download('peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024', 'https://lasse-peters.net/pub/contingency-games/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Contingency Games for Multi-Agent Interaction [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n  <a href=\"https://ieeexplore.ieee.org/document/10400882/\"\n     onclick=\"log_download('peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024', 'https://ieeexplore.ieee.org/document/10400882/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Contingency Games for Multi-Agent Interaction [link]\"\n       title=\"RA-L\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">RA-L</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/LRA.2024.3354548\"\n     onclick=\"log_download('peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024', 'http://doi.org/10.1109/LRA.2024.3354548', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('peters-bajcsy-chiu-fridovichkeil-laine-ferranti-alonsomora-contingencygamesformultiagentinteraction-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{peters_contingency_2024,\n\ttitle = {Contingency {Games} for {Multi}-{Agent} {Interaction}},\n\tvolume = {9},\n\tissn = {2377-3766, 2377-3774},\n\turl = {paper=https://arxiv.org/abs/2304.05483 website=https://lasse-peters.net/pub/contingency-games/ RA-L=https://ieeexplore.ieee.org/document/10400882/},\n\tdoi = {10.1109/LRA.2024.3354548},\n\tabstract = {Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot’s actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-01-29},\n\tjournal = {IEEE Robotics and Automation Letters},\n\tauthor = {Peters, L. and Bajcsy, A. and Chiu, C.Y. and Fridovich-Keil, D. and Laine, F. and Ferranti, L. and Alonso-Mora, J.},\n\tmonth = mar,\n\tyear = {2024},\n\tpages = {2208--2215},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot’s actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024', 'https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf', event);\">\n    Battery Identification with Cubic Spline and Moving Horizon Estimation for Mobile Robots.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shokri, M.; Lyons, L.; Pequito, S.;  and <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Control Systems Technology</i>.  2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf\"\n     onclick=\"log_download('shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024', 'https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Battery Identification with Cubic Spline and Moving Horizon Estimation for Mobile Robots [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TCST.2024.3380950\"\n     onclick=\"log_download('shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024', 'http://doi.org/10.1109/TCST.2024.3380950', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shokri-lyons-pequito-ferranti-batteryidentificationwithcubicsplineandmovinghorizonestimationformobilerobots-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{shokri_battery_2024,\n\ttitle = {Battery {Identification} with {Cubic} {Spline} and {Moving} {Horizon} {Estimation} for {Mobile} {Robots}},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2024/03/Battery_modeling_and_estimation_TCST_2024.pdf},\n\tdoi = {10.1109/TCST.2024.3380950},\n\tabstract = {We propose a novel approach to track the state of charge (SoC) of batteries in mobile robots to improve their capabilities. The batteries’ status is critical to accomplish their mission, but limited battery life can be a challenge. Our methodology focuses on modeling and estimating the SoC of batteries through system identification and fractional-order models. These models are flexible and can adjust to transient responses, allowing for accurate estimation of battery characteristics. Specifically, we use cubic spline interpolation to obtain the open circuit voltage (OCV) and the different resistors of the battery model. To estimate the SoC, we deploy a novel approach based on the moving horizon estimation (MHE) algorithm, which is suitable for handling poor initial estimation and constraints on the battery model. We consider the constraint on the peak discharging current, which can limit the performance of mobile robots in low-battery mode. We validate our approach by applying system identification and MHE to data from a mobile robot. The results show that our method accurately estimates the SoC despite poor initial values, enabling improved performance for mobile robots.},\n\tlanguage = {en},\n\tjournal = {IEEE Transactions on Control Systems Technology},\n\tauthor = {Shokri, M. and Lyons, L. and Pequito, S. and Ferranti, Laura},\n\tyear = {2024},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We propose a novel approach to track the state of charge (SoC) of batteries in mobile robots to improve their capabilities. The batteries’ status is critical to accomplish their mission, but limited battery life can be a challenge. Our methodology focuses on modeling and estimating the SoC of batteries through system identification and fractional-order models. These models are flexible and can adjust to transient responses, allowing for accurate estimation of battery characteristics. Specifically, we use cubic spline interpolation to obtain the open circuit voltage (OCV) and the different resistors of the battery model. To estimate the SoC, we deploy a novel approach based on the moving horizon estimation (MHE) algorithm, which is suitable for handling poor initial estimation and constraints on the battery model. We consider the constraint on the peak discharging current, which can limit the performance of mobile robots in low-battery mode. We validate our approach by applying system identification and MHE to data from a mobile robot. The results show that our method accurately estimates the SoC despite poor initial values, enabling improved performance for mobile robots.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2402.07602.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024', 'https://arxiv.org/pdf/2402.07602.pdf', event);\">\n    DART: A Compact Platform for Autonomous Driving Research.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lyons, L.; Niesten, T.;  and <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n   2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2402.07602.pdf\"\n     onclick=\"log_download('lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024', 'https://arxiv.org/pdf/2402.07602.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"DART: A Compact Platform for Autonomous Driving Research [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://github.com/Lorenzo-Lyons/DART\"\n     onclick=\"log_download('lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024', 'https://github.com/Lorenzo-Lyons/DART', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"DART: A Compact Platform for Autonomous Driving Research [link]\"\n       title=\"Code\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Code</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lyons-niesten-ferranti-dartacompactplatformforautonomousdrivingresearch-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{lyons_dart_2024,\n\ttitle = {{DART}: {A} {Compact} {Platform} for {Autonomous} {Driving} {Research}},\n\turl = {paper=https://arxiv.org/pdf/2402.07602.pdf code=https://github.com/Lorenzo-Lyons/DART},\n\tabstract = {This paper presents the design of a research platform for autonomous driving applications, the Delft’s Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle’s models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.},\n\tlanguage = {en},\n\tauthor = {Lyons, L. and Niesten, T. and Ferranti, L.},\n\tyear = {2024},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents the design of a research platform for autonomous driving applications, the Delft’s Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle’s models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (7)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2310.19511\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023', 'http://arxiv.org/abs/2310.19511', event);\">\n    Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Boldrer, M.; Serra-Gomez, A.; Lyons, L.; Alonso-Mora, J.;  and <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n  October 2023.\n  <span class=\"note\">arXiv:2310.19511 [cs]</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2310.19511\"\n     onclick=\"log_download('boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023', 'http://arxiv.org/abs/2310.19511', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=ZCm-KYHxNG4\"\n     onclick=\"log_download('boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023', 'https://www.youtube.com/watch?v=ZCm-KYHxNG4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n  <a href=\"https://github.com/manuelboldrer/RLB\"\n     onclick=\"log_download('boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023', 'https://github.com/manuelboldrer/RLB', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees [link]\"\n       title=\"Code\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Code</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>6 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('boldrer-serragomez-lyons-alonsomora-ferranti-rulebasedlloydalgorithmformultirobotmotionplanningandcontrolwithsafetyandconvergenceguarantees-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{boldrer_rule-based_2023,\n\ttitle = {Rule-{Based} {Lloyd} {Algorithm} for {Multi}-{Robot} {Motion} {Planning} and {Control} with {Safety} and {Convergence} {Guarantees}},\n\turl = {paper=http://arxiv.org/abs/2310.19511 video=https://www.youtube.com/watch?v=ZCm-KYHxNG4 code=https://github.com/manuelboldrer/RLB},\n\tabstract = {This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to nonnegligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots.},\n\tlanguage = {en},\n\turldate = {2023-11-02},\n\tauthor = {Boldrer, M. and Serra-Gomez, A. and Lyons, L. and Alonso-Mora, J. and Ferranti, L.},\n\tmonth = oct,\n\tyear = {2023},\n\tnote = {arXiv:2310.19511 [cs]},\n\tkeywords = {Computer Science - Robotics, key\\_automotive, key\\_collision\\_avoidance, key\\_control, key\\_motion\\_planning, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to nonnegligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lodder-vanderploeg-ferranti-silvas-optimizationbasedfaultmitigationforsafeautomateddriving-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2303.18165.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lodder-vanderploeg-ferranti-silvas-optimizationbasedfaultmitigationforsafeautomateddriving-2023', 'https://arxiv.org/pdf/2303.18165.pdf', event);\">\n    Optimization-based Fault Mitigation for Safe Automated Driving.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lodder, N,; van der Ploeg, C.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Silvas, E.\n</span>\n\n  \n\n</span>\n\n  In <i>IFAC World Congress</i>, Japan,  2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2303.18165.pdf\"\n     onclick=\"log_download('lodder-vanderploeg-ferranti-silvas-optimizationbasedfaultmitigationforsafeautomateddriving-2023', 'https://arxiv.org/pdf/2303.18165.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Optimization-based Fault Mitigation for Safe Automated Driving [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lodder-vanderploeg-ferranti-silvas-optimizationbasedfaultmitigationforsafeautomateddriving-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lodder-vanderploeg-ferranti-silvas-optimizationbasedfaultmitigationforsafeautomateddriving-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{lodder_n_optimization-based_2023,\n\taddress = {Japan},\n\ttitle = {Optimization-based {Fault} {Mitigation} for {Safe} {Automated} {Driving}},\n\turl = {paper=https://arxiv.org/pdf/2303.18165.pdf},\n\tabstract = {With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and\nidentification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decisionmaking approaches that a fault-affected vehicle could employ to manage any faults. Furthermore,\nwe show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.},\n\tbooktitle = {{IFAC} {World} {Congress}},\n\tauthor = {{Lodder, N,} and {van der Ploeg, C.} and {Ferranti, L.} and {Silvas, E.}},\n\tyear = {2023},\n\tkeywords = {key\\_automotive, key\\_control, key\\_fault\\_tolerant, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and identification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decisionmaking approaches that a fault-affected vehicle could employ to manage any faults. Furthermore, we show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2307.01070.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023', 'https://arxiv.org/pdf/2307.01070.pdf', event);\">\n    Scenario-Based Motion Planning with Bounded Probability of Collision.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  de Groot, O.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Gavrila, D. M.;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n   2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2307.01070.pdf\"\n     onclick=\"log_download('degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023', 'https://arxiv.org/pdf/2307.01070.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Scenario-Based Motion Planning with Bounded Probability of Collision [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=-rqoTICmEO4\"\n     onclick=\"log_download('degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023', 'https://www.youtube.com/watch?v=-rqoTICmEO4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scenario-Based Motion Planning with Bounded Probability of Collision [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('degroot-ferranti-gavrila-alonsomora-scenariobasedmotionplanningwithboundedprobabilityofcollision-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{de_groot_scenario-based_2023,\n\ttitle = {Scenario-{Based} {Motion} {Planning} with {Bounded} {Probability} of {Collision}},\n\turl = {paper=https://arxiv.org/pdf/2307.01070.pdf video=https://www.youtube.com/watch?v=-rqoTICmEO4},\n\tabstract = {Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles&#x27; trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans},\n\tauthor = {de Groot, O. and Ferranti, L. and Gavrila, D. M. and Alonso-Mora, J.},\n\tyear = {2023},\n\tkeywords = {key\\_collision\\_avoidance, key\\_control, key\\_motion\\_planning, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023', 'https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf', event);\">\n    Globally Guided Trajectory Planning in Dynamic Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  de Groot, O.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Gavrila, D. M.;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  In <i>2023 IEEE International Conference on Robotics and Automation (ICRA)</i>, London, UK,  2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf\"\n     onclick=\"log_download('degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023', 'https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Globally Guided Trajectory Planning in Dynamic Environments [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=tkRbsAuSTrA\"\n     onclick=\"log_download('degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023', 'https://www.youtube.com/watch?v=tkRbsAuSTrA', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Globally Guided Trajectory Planning in Dynamic Environments [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('degroot-ferranti-gavrila-alonsomora-globallyguidedtrajectoryplanningindynamicenvironments-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{de_groot_o_globally_2023,\n\taddress = {London, UK},\n\ttitle = {Globally {Guided} {Trajectory} {Planning} in {Dynamic} {Environments}},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2023/03/ICRA2023_homotopy-3.pdf video=https://www.youtube.com/watch?v=tkRbsAuSTrA},\n\tabstract = {Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving\nbehaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the\ntopology information. The most suitable high-level trajectory\nguides a local optimization-based planner, resulting in fast and\nsafe motion plans.We validate the proposed planner on a mobile\nrobot in simulation and real-world experiments.},\n\tbooktitle = {2023 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\n\tauthor = {{de Groot, O.} and {Ferranti, L.} and {Gavrila, D. M.} and {Alonso-Mora, J.}},\n\tyear = {2023},\n\tkeywords = {key\\_collision\\_avoidance, key\\_motion\\_planning, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving behaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the topology information. The most suitable high-level trajectory guides a local optimization-based planner, resulting in fast and safe motion plans.We validate the proposed planner on a mobile robot in simulation and real-world experiments.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"peters-rubiesroyo-tomlin-ferranti-alonsomora-stachniss-fridovichkeil-learningplayersobjectivesincontinuousdynamicgamesfrompartialstateobservations-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2302.01999.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'peters-rubiesroyo-tomlin-ferranti-alonsomora-stachniss-fridovichkeil-learningplayersobjectivesincontinuousdynamicgamesfrompartialstateobservations-2023', 'https://arxiv.org/pdf/2302.01999.pdf', event);\">\n    Learning Players' Objectives in Continuous Dynamic Games from Partial State Observations.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Peters, L.; Rubies-Royo, Vicenç; Tomlin, C. J; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Alonso-Mora, J.; Stachniss, C.;  and Fridovich-Keil, D\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Robotics Research</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2302.01999.pdf\"\n     onclick=\"log_download('peters-rubiesroyo-tomlin-ferranti-alonsomora-stachniss-fridovichkeil-learningplayersobjectivesincontinuousdynamicgamesfrompartialstateobservations-2023', 'https://arxiv.org/pdf/2302.01999.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Learning Players&#x27; Objectives in Continuous Dynamic Games from Partial State Observations [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/peters-rubiesroyo-tomlin-ferranti-alonsomora-stachniss-fridovichkeil-learningplayersobjectivesincontinuousdynamicgamesfrompartialstateobservations-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('peters-rubiesroyo-tomlin-ferranti-alonsomora-stachniss-fridovichkeil-learningplayersobjectivesincontinuousdynamicgamesfrompartialstateobservations-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{peters_l_learning_2023,\n\ttitle = {Learning {Players}&#x27; {Objectives} in {Continuous} {Dynamic} {Games} from {Partial} {State} {Observations}},\n\turl = {paper=https://arxiv.org/pdf/2302.01999.pdf},\n\tabstract = {Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players&#x27; objectives. In this work, we address this issue by proposing a novel method for learning players&#x27; objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players&#x27; preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.},\n\tjournal = {International Journal of Robotics Research},\n\tauthor = {{Peters, L.} and {Rubies-Royo, Vicenç} and {Tomlin, C. J} and {Ferranti, L.} and {Alonso-Mora, J.} and Stachniss, C. and Fridovich-Keil, D},\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sibona-luijkx-vanderheijden-ferranti-indri-evalueactionaproposalforpolicyevaluationinsimulationtosupportinteractiveimitationlearning-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sibona-luijkx-vanderheijden-ferranti-indri-evalueactionaproposalforpolicyevaluationinsimulationtosupportinteractiveimitationlearning-2023', 'https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf', event);\">\n    EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sibona, F.; Luijkx, J.; van der Heijden, B.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Indri, M.\n</span>\n\n  \n\n</span>\n\n  In <i>IEEE INDIN 2023</i>,  2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf\"\n     onclick=\"log_download('sibona-luijkx-vanderheijden-ferranti-indri-evalueactionaproposalforpolicyevaluationinsimulationtosupportinteractiveimitationlearning-2023', 'https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sibona-luijkx-vanderheijden-ferranti-indri-evalueactionaproposalforpolicyevaluationinsimulationtosupportinteractiveimitationlearning-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sibona-luijkx-vanderheijden-ferranti-indri-evalueactionaproposalforpolicyevaluationinsimulationtosupportinteractiveimitationlearning-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sibona_evalueaction_2023,\n\ttitle = {{EValueAction}: a proposal for policy evaluation in simulation to support interactive imitation learning},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf},\n\tabstract = {The up-and-coming concept of Industry 5.0 foresees\nhuman-centric flexible production lines, where collaborative\nrobots support human workforce. In order to allow a seamless\ncollaboration between intelligent robots and human workers,\ndesigning solutions for non-expert users is crucial. Learning from\ndemonstration emerged as the enabling approach to address such\na problem. However, more focus should be put on finding safe\nsolutions which optimize the cost associated with the demonstrations\ncollection process. This paper introduces a preliminary outline\nof a system, namely EValueAction (EVA), designed to assist\nthe human in the process of collecting interactive demonstrations\ntaking advantage of simulation to safely avoid failures. A policy\nis pre-trained with human-demonstrations and, where needed,\nnew informative data are interactively gathered and aggregated\nto iteratively improve the initial policy. A trial case study further\nreinforces the relevance of the work by demonstrating the crucial\nrole of informative demonstrations for generalization.},\n\tbooktitle = {{IEEE} {INDIN} 2023},\n\tauthor = {Sibona, F. and Luijkx, J. and van der Heijden, B. and Ferranti, L. and Indri, M.},\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The up-and-coming concept of Industry 5.0 foresees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling approach to address such a problem. However, more focus should be put on finding safe solutions which optimize the cost associated with the demonstrations collection process. This paper introduces a preliminary outline of a system, namely EValueAction (EVA), designed to assist the human in the process of collecting interactive demonstrations taking advantage of simulation to safely avoid failures. A policy is pre-trained with human-demonstrations and, where needed, new informative data are interactively gathered and aggregated to iteratively improve the initial policy. A trial case study further reinforces the relevance of the work by demonstrating the crucial role of informative demonstrations for generalization.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023', 'https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf', event);\">\n    Curvature-Aware Model Predictive Contouring Control.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lyons, L.;  and <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>2023 IEEE International Conference on Robotics and Automation (ICRA)</i>,  2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf\"\n     onclick=\"log_download('lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023', 'https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Curvature-Aware Model Predictive Contouring Control [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=6-E3I99D2sc\"\n     onclick=\"log_download('lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023', 'https://www.youtube.com/watch?v=6-E3I99D2sc', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Curvature-Aware Model Predictive Contouring Control [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>19 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lyons-ferranti-curvatureawaremodelpredictivecontouringcontrol-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{lyons_l_curvature-aware_2023,\n\ttitle = {Curvature-{Aware} {Model} {Predictive} {Contouring} {Control}},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2023/02/Lyons_ICRA_2023.pdf  video=https://www.youtube.com/watch?v=6-E3I99D2sc},\n\tabstract = {We present a novel Curvature-Aware Model Predictive Contouring Control (CA-MPCC) formulation for mobile\nrobotics motion planning. Our method aims at generalizing\nthe traditional contouring control formulation derived from\nmachining to autonomous driving applications. The proposed\ncontroller is able of handling sharp curvatures in the reference\npath while subject to non-linear constraints, such as lane\nboundaries and dynamic obstacle collision avoidance. Compared to a standard MPCC formulation, our method improves\nthe reliability of the path-following algorithm and simplifies the\ntuning, while preserving real-time capabilities. We validate our\nfindings in both simulations and experiments on a scaled-down\ncar-like robot.},\n\tbooktitle = {2023 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\n\tauthor = {{Lyons, L.} and {Ferranti, L.}},\n\tyear = {2023},\n\tkeywords = {key\\_automotive, key\\_collision\\_avoidance, key\\_motion\\_planning, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We present a novel Curvature-Aware Model Predictive Contouring Control (CA-MPCC) formulation for mobile robotics motion planning. Our method aims at generalizing the traditional contouring control formulation derived from machining to autonomous driving applications. The proposed controller is able of handling sharp curvatures in the reference path while subject to non-linear constraints, such as lane boundaries and dynamic obstacle collision avoidance. Compared to a standard MPCC formulation, our method improves the reliability of the path-following algorithm and simplifies the tuning, while preserving real-time capabilities. We validate our findings in both simulations and experiments on a scaled-down car-like robot.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2211.08304.pdf,\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022', 'https://arxiv.org/pdf/2211.08304.pdf,', event);\">\n    PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Luijkx, J.; Ajanovic, Z.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Kober, J.\n</span>\n\n  \n\n</span>\n\n  In <i>NeurIPS Workshop on Robot Learning</i>, November 2022. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2211.08304.pdf,\"\n     onclick=\"log_download('luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022', 'https://arxiv.org/pdf/2211.08304.pdf,', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=q8S2Ua41Lik,\"\n     onclick=\"log_download('luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022', 'https://www.youtube.com/watch?v=q8S2Ua41Lik,', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n  <a href=\"https://partnr-learn.github.io/\"\n     onclick=\"log_download('luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022', 'https://partnr-learn.github.io/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('luijkx-ajanovic-ferranti-kober-partnrpickandplaceambiguityresolvingbytrustworthyinteractivelearning-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{luijkx_j_partnr_2022,\n\ttitle = {{PARTNR}: {Pick} and place {Ambiguity} {Resolving} by {Trustworthy} {iNteractive} {leaRning}},\n\turl = {paper=https://arxiv.org/pdf/2211.08304.pdf, video=https://www.youtube.com/watch?v=q8S2Ua41Lik, website=https://partnr-learn.github.io/},\n\tabstract = {Several recent works show impressive results in mapping language-based human\ncommands and image scene observations to direct robot executable policies (e.g.,\npick and place poses). However, these approaches do not consider the uncertainty\nof the trained policy and simply always execute actions suggested by the current\npolicy as the most probable ones. This makes them vulnerable to domain shift and\ninefficient in the number of required demonstrations. We extend previous works\nand present the PARTNR algorithm that can detect ambiguities in the trained policy\nby analyzing multiple modalities in the pick and place poses using topological\nanalysis. PARTNR employs an adaptive, sensitivity-based, gating function that\ndecides if additional user demonstrations are required. User demonstrations are\naggregated to the dataset and used for subsequent training. In this way, the policy\ncan adapt promptly to domain shift and it can minimize the number of required\ndemonstrations for a well-trained policy. The adaptive threshold enables to achieve\nthe user-acceptable level of ambiguity to execute the policy autonomously and in\nturn, increase the trustworthiness of our system. We demonstrate the performance\nof PARTNR in a table-top pick and place task.},\n\tbooktitle = {{NeurIPS} {Workshop} on {Robot} {Learning}},\n\tauthor = {{Luijkx, J.} and {Ajanovic, Z.} and {Ferranti, L.} and {Kober, J.}},\n\tmonth = nov,\n\tyear = {2022},\n\tkeywords = {key\\_manipulator, key\\_robot\\_learning},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses). However, these approaches do not consider the uncertainty of the trained policy and simply always execute actions suggested by the current policy as the most probable ones. This makes them vulnerable to domain shift and inefficient in the number of required demonstrations. We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modalities in the pick and place poses using topological analysis. PARTNR employs an adaptive, sensitivity-based, gating function that decides if additional user demonstrations are required. User demonstrations are aggregated to the dataset and used for subsequent training. In this way, the policy can adapt promptly to domain shift and it can minimize the number of required demonstrations for a well-trained policy. The adaptive threshold enables to achieve the user-acceptable level of ambiguity to execute the policy autonomously and in turn, increase the trustworthiness of our system. We demonstrate the performance of PARTNR in a table-top pick and place task.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022', 'https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf', event);\">\n    Time-inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Manuel Boldrer; Lorenzo Lyons; Luigi Palopoli; Daniele Fontanelli;  and Laura Ferranti\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Robotics and Automation Letters</i>. November 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf\"\n     onclick=\"log_download('manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022', 'https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Time-inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=pMJR5q4jNSs\"\n     onclick=\"log_download('manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022', 'https://www.youtube.com/watch?v=pMJR5q4jNSs', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Time-inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>17 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('manuelboldrer-lorenzolyons-luigipalopoli-danielefontanelli-lauraferranti-timeinvertedkuramotomodelmeetslissajouscurvesmultirobotpersistentmonitoringandtargetdetection-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{manuel_boldrer_time-inverted_2022,\n\ttitle = {Time-inverted {Kuramoto} {Model} {Meets} {Lissajous} {Curves}: {Multi}-{Robot} {Persistent} {Monitoring} and {Target} {Detection}},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2022/11/Kuramoto_Lissajous_final.pdf video=https://www.youtube.com/watch?v=pMJR5q4jNSs},\n\tabstract = {This work proposes a distributed strategy to achieve\nboth persistent monitoring and target detection in a rectangular\nand obstacle-free environment. Each robot has to repeatedly\nfollow a smooth trajectory and avoid collisions with other robots.\nTo achieve this goal, we rely on the time-inverted Kuramoto\ndynamics and the use of Lissajous curves. We analyze the\nresiliency of the system to perturbations or temporary failures,\nand we validate our approach through both simulations and\nexperiments on real robotic platforms. In the latter, we adopt\nModel Predictive Contouring Control as a low level controller to\nminimize the tracking error while accounting for the robots’\ndynamical constraints and the control inputs saturation. The\nresults obtained in the experiments are in accordance with the\nsimulations},\n\tjournal = {IEEE Robotics and Automation Letters},\n\tauthor = {{Manuel Boldrer} and {Lorenzo Lyons} and {Luigi Palopoli} and {Daniele Fontanelli} and {Laura Ferranti}},\n\tmonth = nov,\n\tyear = {2022},\n\tkeywords = {key\\_automotive, key\\_control, key\\_experiments, key\\_mpc},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This work proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. We analyze the resiliency of the system to perturbations or temporary failures, and we validate our approach through both simulations and experiments on real robotic platforms. In the latter, we adopt Model Predictive Contouring Control as a low level controller to minimize the tracking error while accounting for the robots’ dynamical constraints and the control inputs saturation. The results obtained in the experiments are in accordance with the simulations\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022', 'https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf', event);\">\n    Distributed Nonlinear Trajectory Optimization for Multi-robot Motion Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Lyons, L.; Negenborn, R.R.; Keviczky, T.;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Control Systems Technology</i>. September 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf\"\n     onclick=\"log_download('ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022', 'https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Distributed Nonlinear Trajectory Optimization for Multi-robot Motion Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=o2W2OhNf5yc\"\n     onclick=\"log_download('ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022', 'https://www.youtube.com/watch?v=o2W2OhNf5yc', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Distributed Nonlinear Trajectory Optimization for Multi-robot Motion Planning [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TCST.2022.3211130\"\n     onclick=\"log_download('ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022', 'http://doi.org/10.1109/TCST.2022.3211130', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-lyons-negenborn-keviczky-alonsomora-distributednonlineartrajectoryoptimizationformultirobotmotionplanning-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ferranti_distributed_2022,\n\ttitle = {Distributed {Nonlinear} {Trajectory} {Optimization} for {Multi}-robot {Motion} {Planning}},\n\turl = {paper=https://r2clab.com/wp-content/uploads/2022/10/tcsc_v2_ferranti-copy-1.pdf video=https://www.youtube.com/watch?v=o2W2OhNf5yc},\n\tdoi = {10.1109/TCST.2022.3211130},\n\tabstract = {This work presents a method for multi-robot\ncoordination based on a novel distributed nonlinear model\npredictive control formulation for trajectory optimization\nand its modified version to mitigate the effects of packet\nlosses and delays in the communication among the robots.\nOur algorithms consider that each robot is equipped with\nan on-board computation unit to solve a local control\nproblem and communicate with neighboring autonomous\nrobots via a wireless network. The difference between the\ntwo proposed methods is in the way the robots exchange\ninformation to coordinate. The information exchange can\noccur in a (i) synchronous or (ii) asynchronous fashion. By\nrelying on the theory of the nonconvex alternating direction\nmethod of multipliers, we show that the proposed solutions\nconverge to a (local) solution of the centralized problem.\nFor both algorithms, the communication exchange\npreserves the safety of the robots, that is, collisions\nwith neighboring autonomous robots are prevented. The\nproposed approaches can be applied to various multi-robot\nscenarios and robot models. In this work, we assess our\nmethods, both in simulation and with experiments, for the\ncoordination of a team of autonomous vehicles in (a) an\nunsupervised intersection crossing and (b) a platooning\nscenarios.},\n\tjournal = {IEEE Transactions on Control Systems Technology},\n\tauthor = {Ferranti, L. and {Lyons, L.} and {Negenborn, R.R.} and {Keviczky, T.} and {Alonso-Mora, J.}},\n\tmonth = sep,\n\tyear = {2022},\n\tkeywords = {key\\_automotive, key\\_co, key\\_collision\\_avoidance, key\\_motion\\_planning, key\\_mpc, key\\_multirobot},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an on-board computation unit to solve a local control problem and communicate with neighboring autonomous robots via a wireless network. The difference between the two proposed methods is in the way the robots exchange information to coordinate. The information exchange can occur in a (i) synchronous or (ii) asynchronous fashion. By relying on the theory of the nonconvex alternating direction method of multipliers, we show that the proposed solutions converge to a (local) solution of the centralized problem. For both algorithms, the communication exchange preserves the safety of the robots, that is, collisions with neighboring autonomous robots are prevented. The proposed approaches can be applied to various multi-robot scenarios and robot models. In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in (a) an unsupervised intersection crossing and (b) a platooning scenarios.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2203.02381\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022', 'http://arxiv.org/abs/2203.02381', event);\">\n    Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lodel, M.; Brito, B.; Serra-Gómez, A.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Babuška, R.;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  March 2022.\n  <span class=\"note\">Accepted for publication in the Proc. of ICRA 2022</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2203.02381\"\n     onclick=\"log_download('lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022', 'http://arxiv.org/abs/2203.02381', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=qxabfC9I66k\"\n     onclick=\"log_download('lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022', 'https://www.youtube.com/watch?v=qxabfC9I66k', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lodel-brito-serragmez-ferranti-babuka-alonsomora-wheretolooknextlearningviewpointrecommendationsforinformativetrajectoryplanning-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{lodel_where_2022,\n\ttitle = {Where to {Look} {Next}: {Learning} {Viewpoint} {Recommendations} for {Informative} {Trajectory} {Planning}},\n\tshorttitle = {Where to {Look} {Next}},\n\turl = {paper=http://arxiv.org/abs/2203.02381 video=https://www.youtube.com/watch?v=qxabfC9I66k},\n\tpublisher = {arXiv},\n\tauthor = {Lodel, M. and Brito, B. and Serra-Gómez, A. and Ferranti, L. and Babuška, R. and Alonso-Mora, J.},\n\tmonth = mar,\n\tyear = {2022},\n\tnote = {Accepted for publication in the Proc. of ICRA 2022},\n\tkeywords = {key\\_drones, key\\_exploration, key\\_mpc, key\\_reinforcement\\_learning, type\\_conference},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2205.00291\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022', 'http://arxiv.org/abs/2205.00291', event);\">\n    Learning Mixed Strategies in Trajectory Games.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Peters, L.; Fridovich-Keil, D.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Stachniss, C.; Alonso-Mora, J.;  and Laine, F.\n</span>\n\n  \n\n</span>\n\n  In <i>Robotics: Science and Systems (RSS)</i>,  2022. \n  <span class=\"note\">Accepted for publication in the Proc. of RSS 2022</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2205.00291\"\n     onclick=\"log_download('peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022', 'http://arxiv.org/abs/2205.00291', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning Mixed Strategies in Trajectory Games [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=gT52cKH9pvg\"\n     onclick=\"log_download('peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022', 'https://www.youtube.com/watch?v=gT52cKH9pvg', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning Mixed Strategies in Trajectory Games [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n  <a href=\"https://lasse-peters.net/pub/lifted-games/\"\n     onclick=\"log_download('peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022', 'https://lasse-peters.net/pub/lifted-games/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning Mixed Strategies in Trajectory Games [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=gT52cKH9pvg\"\n     onclick=\"log_download('peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022', 'https://www.youtube.com/watch?v=gT52cKH9pvg', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning Mixed Strategies in Trajectory Games [link]\"\n       title=\"Talk\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Talk</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('peters-fridovichkeil-ferranti-stachniss-alonsomora-laine-learningmixedstrategiesintrajectorygames-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{peters_learning_2022,\n\ttitle = {Learning {Mixed} {Strategies} in {Trajectory} {Games}},\n\turl = {paper=http://arxiv.org/abs/2205.00291 video=https://www.youtube.com/watch?v=gT52cKH9pvg website=https://lasse-peters.net/pub/lifted-games/ talk=https://www.youtube.com/watch?v=gT52cKH9pvg},\n\tabstract = {In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional “predict then plan” approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive “mixed” strategies. We validate our approach on a number of experiments using the pursuit-evasion game “tag.”},\n\tbooktitle = {Robotics: {Science} and {Systems} ({RSS})},\n\tauthor = {Peters, L. and Fridovich-Keil, D. and Ferranti, L. and Stachniss, C. and Alonso-Mora, J. and Laine, F.},\n\tyear = {2022},\n\tnote = {Accepted for publication in the Proc. of RSS 2022},\n\tkeywords = {key\\_game\\_theory, key\\_multirobot, type\\_conference},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional “predict then plan” approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive “mixed” strategies. We validate our approach on a number of experiments using the pursuit-evasion game “tag.”\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/9636408/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021', 'https://ieeexplore.ieee.org/document/9636408/', event);\">\n    DeepKoCo: Efficient latent planning with a task-relevant Koopman representation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  van der Heijden, B.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Kober, J.;  and Babuska, R.\n</span>\n\n  \n\n</span>\n\n  In <i>2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</i>, pages 183–189, Prague, Czech Republic, September 2021. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9636408/\"\n     onclick=\"log_download('vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021', 'https://ieeexplore.ieee.org/document/9636408/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"DeepKoCo: Efficient latent planning with a task-relevant Koopman representation [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://youtu.be/YyopLDu7xIo\"\n     onclick=\"log_download('vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021', 'https://youtu.be/YyopLDu7xIo', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"DeepKoCo: Efficient latent planning with a task-relevant Koopman representation [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/IROS51168.2021.9636408\"\n     onclick=\"log_download('vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021', 'http://doi.org/10.1109/IROS51168.2021.9636408', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>6 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vanderheijden-ferranti-kober-babuska-deepkocoefficientlatentplanningwithataskrelevantkoopmanrepresentation-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{van_der_heijden_deepkoco_2021,\n\taddress = {Prague, Czech Republic},\n\ttitle = {{DeepKoCo}: {Efficient} latent planning with a task-relevant {Koopman} representation},\n\turl = {paper=https://ieeexplore.ieee.org/document/9636408/ video=https://youtu.be/YyopLDu7xIo},\n\tdoi = {10.1109/IROS51168.2021.9636408},\n\tbooktitle = {2021 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},\n\tpublisher = {IEEE},\n\tauthor = {van der Heijden, B. and Ferranti, L. and Kober, J. and Babuska, R.},\n\tmonth = sep,\n\tyear = {2021},\n\tkeywords = {key\\_reinforcement\\_learning, key\\_representation\\_learning, type\\_conference},\n\tpages = {183--189},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/9669239/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021', 'https://ieeexplore.ieee.org/document/9669239/', event);\">\n    Active Safety System for Semi-Autonomous Teleoperated Vehicles.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Saparia, S.; Schimpe, A.;  and <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)</i>, pages 141–147, Nagoya, Japan, July 2021. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9669239/\"\n     onclick=\"log_download('saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021', 'https://ieeexplore.ieee.org/document/9669239/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Active Safety System for Semi-Autonomous Teleoperated Vehicles [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/IVWorkshops54471.2021.9669239\"\n     onclick=\"log_download('saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021', 'http://doi.org/10.1109/IVWorkshops54471.2021.9669239', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saparia-schimpe-ferranti-activesafetysystemforsemiautonomousteleoperatedvehicles-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{saparia_active_2021,\n\taddress = {Nagoya, Japan},\n\ttitle = {Active {Safety} {System} for {Semi}-{Autonomous} {Teleoperated} {Vehicles}},\n\turl = {https://ieeexplore.ieee.org/document/9669239/},\n\tdoi = {10.1109/IVWorkshops54471.2021.9669239},\n\tbooktitle = {2021 {IEEE} {Intelligent} {Vehicles} {Symposium} {Workshops} ({IV} {Workshops})},\n\tpublisher = {IEEE},\n\tauthor = {Saparia, S. and Schimpe, A. and Ferranti, L.},\n\tmonth = jul,\n\tyear = {2021},\n\tkeywords = {key\\_automotive, key\\_mpc, key\\_teleoperation, type\\_conference},\n\tpages = {141--147},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/9410362/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021', 'https://ieeexplore.ieee.org/document/9410362/', event);\">\n    Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  de Groot, O.; Brito, B.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Gavrila, D. M.;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Robotics and Automation Letters</i>, 6(3): 5389–5396. July 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9410362/\"\n     onclick=\"log_download('degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021', 'https://ieeexplore.ieee.org/document/9410362/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=YONmA4EGmtI\"\n     onclick=\"log_download('degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021', 'https://www.youtube.com/watch?v=YONmA4EGmtI', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/LRA.2021.3074866\"\n     onclick=\"log_download('degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021', 'http://doi.org/10.1109/LRA.2021.3074866', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('degroot-brito-ferranti-gavrila-alonsomora-scenariobasedtrajectoryoptimizationinuncertaindynamicenvironments-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{de_groot_scenario-based_2021,\n\ttitle = {Scenario-{Based} {Trajectory} {Optimization} in {Uncertain} {Dynamic} {Environments}},\n\tvolume = {6},\n\turl = {paper=https://ieeexplore.ieee.org/document/9410362/ video=https://www.youtube.com/watch?v=YONmA4EGmtI},\n\tdoi = {10.1109/LRA.2021.3074866},\n\tnumber = {3},\n\tjournal = {IEEE Robotics and Automation Letters},\n\tauthor = {de Groot, O. and Brito, B. and Ferranti, L. and Gavrila, D. M. and Alonso-Mora, J.},\n\tmonth = jul,\n\tyear = {2021},\n\tkeywords = {key\\_collision\\_avoidance, key\\_motion\\_planning, key\\_mpc, key\\_scenario\\_optimization, type\\_journal},\n\tpages = {5389--5396},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021', 'https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240', event);\">\n    Integrated nonlinear model predictive control for automated driving.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chowdhri, N.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Iribarren, F. S.;  and Shyrokau, B.\n</span>\n\n  \n\n</span>\n\n  <i>Control Engineering Practice</i>, 106: 104654. January 2021.\n  <span class=\"note\">Feature Paper of Control Engineering Practice for June 2022</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240\"\n     onclick=\"log_download('chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021', 'https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Integrated nonlinear model predictive control for automated driving [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.conengprac.2020.104654\"\n     onclick=\"log_download('chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021', 'http://doi.org/10.1016/j.conengprac.2020.104654', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chowdhri-ferranti-iribarren-shyrokau-integratednonlinearmodelpredictivecontrolforautomateddriving-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{chowdhri_integrated_2021,\n\ttitle = {Integrated nonlinear model predictive control for automated driving},\n\tvolume = {106},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0967066120302240},\n\tdoi = {10.1016/j.conengprac.2020.104654},\n\tlanguage = {en},\n\tjournal = {Control Engineering Practice},\n\tauthor = {Chowdhri, N. and Ferranti, L. and Iribarren, F. S. and Shyrokau, B.},\n\tmonth = jan,\n\tyear = {2021},\n\tnote = {Feature Paper of Control Engineering Practice for June 2022},\n\tkeywords = {key\\_automotive, key\\_evasive, key\\_mpc, type\\_journal},\n\tpages = {104654},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"firoozi-ferranti-zhang-nejadnik-borrelli-adistributedmultirobotcoordinationalgorithmfornavigationintightenvironments-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2006.11492\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'firoozi-ferranti-zhang-nejadnik-borrelli-adistributedmultirobotcoordinationalgorithmfornavigationintightenvironments-2020', 'http://arxiv.org/abs/2006.11492', event);\">\n    A Distributed Multi-Robot Coordination Algorithm for Navigation in Tight Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Firoozi, R.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Zhang, X.; Nejadnik, S.;  and Borrelli, F.\n</span>\n\n  \n\n</span>\n\n  June 2020.\n  <span class=\"note\">arXiv:2006.11492 [cs]</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2006.11492\"\n     onclick=\"log_download('firoozi-ferranti-zhang-nejadnik-borrelli-adistributedmultirobotcoordinationalgorithmfornavigationintightenvironments-2020', 'http://arxiv.org/abs/2006.11492', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Distributed Multi-Robot Coordination Algorithm for Navigation in Tight Environments [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/firoozi-ferranti-zhang-nejadnik-borrelli-adistributedmultirobotcoordinationalgorithmfornavigationintightenvironments-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('firoozi-ferranti-zhang-nejadnik-borrelli-adistributedmultirobotcoordinationalgorithmfornavigationintightenvironments-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{firoozi_distributed_2020,\n\ttitle = {A {Distributed} {Multi}-{Robot} {Coordination} {Algorithm} for {Navigation} in {Tight} {Environments}},\n\turl = {http://arxiv.org/abs/2006.11492},\n\tpublisher = {arXiv},\n\tauthor = {Firoozi, R. and Ferranti, L. and Zhang, X. and Nejadnik, S. and Borrelli, F.},\n\tmonth = jun,\n\tyear = {2020},\n\tnote = {arXiv:2006.11492 [cs]},\n\tkeywords = {key\\_collision\\_avoidance, key\\_motion\\_planning, key\\_mpc, key\\_robotics, key\\_splitting, type\\_arxiv},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8955905/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020', 'https://ieeexplore.ieee.org/document/8955905/', event);\">\n    Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pretagostini, F.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Berardo, G.; Ivanov, V.;  and Shyrokau, B.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Access</i>, 8: 10951–10970.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8955905/\"\n     onclick=\"log_download('pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020', 'https://ieeexplore.ieee.org/document/8955905/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ACCESS.2020.2965644\"\n     onclick=\"log_download('pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020', 'http://doi.org/10.1109/ACCESS.2020.2965644', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pretagostini-ferranti-berardo-ivanov-shyrokau-surveyonwheelslipcontroldesignstrategiesevaluationandapplicationtoantilockbrakingsystems-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{pretagostini_survey_2020,\n\ttitle = {Survey on {Wheel} {Slip} {Control} {Design} {Strategies}, {Evaluation} and {Application} to {Antilock} {Braking} {Systems}},\n\tvolume = {8},\n\turl = {https://ieeexplore.ieee.org/document/8955905/},\n\tdoi = {10.1109/ACCESS.2020.2965644},\n\tjournal = {IEEE Access},\n\tauthor = {Pretagostini, F. and Ferranti, L. and Berardo, G. and Ivanov, V. and Shyrokau, B.},\n\tyear = {2020},\n\tkeywords = {key\\_automotive, key\\_control, key\\_mpc, key\\_review, type\\_journal},\n\tpages = {10951--10970},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019', 'https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063', event);\">\n    Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Wan, Y.;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Robust and Nonlinear Control</i>, 29(16): 5412–5428. November 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063\"\n     onclick=\"log_download('ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019', 'https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1002/rnc.4063\"\n     onclick=\"log_download('ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019', 'http://doi.org/10.1002/rnc.4063', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-wan-keviczky-faulttolerantreferencegenerationformodelpredictivecontrolwithactivediagnosisofelevatorjammingfaults-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ferranti_faulttolerant_2019,\n\ttitle = {Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults},\n\tvolume = {29},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/rnc.4063},\n\tdoi = {10.1002/rnc.4063},\n\tlanguage = {en},\n\tnumber = {16},\n\tjournal = {International Journal of Robust and Nonlinear Control},\n\tauthor = {Ferranti, L. and Wan, Y. and Keviczky, T.},\n\tmonth = nov,\n\tyear = {2019},\n\tkeywords = {key\\_aerospace, key\\_fault\\_tolerant, key\\_mpc, type\\_journal},\n\tpages = {5412--5428},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8768044/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019', 'https://ieeexplore.ieee.org/document/8768044/', event);\">\n    Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Brito, B.; Floor, B.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Robotics and Automation Letters</i>, 4(4): 4459–4466. October 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8768044/\"\n     onclick=\"log_download('brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019', 'https://ieeexplore.ieee.org/document/8768044/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=crGTsiiilHo\"\n     onclick=\"log_download('brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019', 'https://www.youtube.com/watch?v=crGTsiiilHo', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/LRA.2019.2929976\"\n     onclick=\"log_download('brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019', 'http://doi.org/10.1109/LRA.2019.2929976', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('brito-floor-ferranti-alonsomora-modelpredictivecontouringcontrolforcollisionavoidanceinunstructureddynamicenvironments-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{brito_model_2019,\n\ttitle = {Model {Predictive} {Contouring} {Control} for {Collision} {Avoidance} in {Unstructured} {Dynamic} {Environments}},\n\tvolume = {4},\n\turl = {paper=https://ieeexplore.ieee.org/document/8768044/ video=https://www.youtube.com/watch?v=crGTsiiilHo},\n\tdoi = {10.1109/LRA.2019.2929976},\n\tnumber = {4},\n\tjournal = {IEEE Robotics and Automation Letters},\n\tauthor = {Brito, B. and Floor, B. and Ferranti, L. and Alonso-Mora, J.},\n\tmonth = oct,\n\tyear = {2019},\n\tkeywords = {key\\_collision\\_avoidance, key\\_motion\\_planning, key\\_mpc, key\\_robotics, type\\_journal},\n\tpages = {4459--4466},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8813899/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019', 'https://ieeexplore.ieee.org/document/8813899/', event);\">\n    SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Brito, B.; Pool, E.; Zheng, Y.; Ensing, R. M.; Happee, R.; Shyrokau, B.; Kooij, J. F. P.; Alonso-Mora, J.;  and Gavrila, D. M.\n</span>\n\n  \n\n</span>\n\n  In <i>2019 IEEE Intelligent Vehicles Symposium (IV)</i>, pages 1660–1666, Paris, France, June 2019. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8813899/\"\n     onclick=\"log_download('ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019', 'https://ieeexplore.ieee.org/document/8813899/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=iISdbfPsR9g\"\n     onclick=\"log_download('ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019', 'https://www.youtube.com/watch?v=iISdbfPsR9g', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users [link]\"\n       title=\"Demo\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Demo</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/IVS.2019.8813899\"\n     onclick=\"log_download('ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019', 'http://doi.org/10.1109/IVS.2019.8813899', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-brito-pool-zheng-ensing-happee-shyrokau-kooij-etal-safevruaresearchplatformfortheinteractionofselfdrivingvehicleswithvulnerableroadusers-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferranti_safevru_2019,\n\taddress = {Paris, France},\n\ttitle = {{SafeVRU}: {A} {Research} {Platform} for the {Interaction} of {Self}-{Driving} {Vehicles} with {Vulnerable} {Road} {Users}},\n\turl = {paper=https://ieeexplore.ieee.org/document/8813899/ demo=https://www.youtube.com/watch?v=iISdbfPsR9g},\n\tdoi = {10.1109/IVS.2019.8813899},\n\tbooktitle = {2019 {IEEE} {Intelligent} {Vehicles} {Symposium} ({IV})},\n\tpublisher = {IEEE},\n\tauthor = {Ferranti, L. and Brito, B. and Pool, E. and Zheng, Y. and Ensing, R. M. and Happee, R. and Shyrokau, B. and Kooij, J. F. P. and Alonso-Mora, J. and Gavrila, D. M.},\n\tmonth = jun,\n\tyear = {2019},\n\tkeywords = {key\\_automotive, key\\_experiments, key\\_motion\\_planning, key\\_mpc, key\\_perception, type\\_conference},\n\tpages = {1660--1666},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8793765/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019', 'https://ieeexplore.ieee.org/document/8793765/', event);\">\n    Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhu, H.; Juhl, J.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  In <i>2019 International Conference on Robotics and Automation (ICRA)</i>, pages 9080–9086, Montreal, QC, Canada, May 2019. IEEE\n  <span class=\"note\">Best paper award in Multi-Robot Systems</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8793765/\"\n     onclick=\"log_download('zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019', 'https://ieeexplore.ieee.org/document/8793765/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.youtube.com/watch?v=Kx14E98Iqng\"\n     onclick=\"log_download('zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019', 'https://www.youtube.com/watch?v=Kx14E98Iqng', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments [link]\"\n       title=\"Video\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Video</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ICRA.2019.8793765\"\n     onclick=\"log_download('zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019', 'http://doi.org/10.1109/ICRA.2019.8793765', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhu-juhl-ferranti-alonsomora-distributedmultirobotformationsplittingandmergingindynamicenvironments-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{zhu_distributed_2019,\n\taddress = {Montreal, QC, Canada},\n\ttitle = {Distributed {Multi}-{Robot} {Formation} {Splitting} and {Merging} in {Dynamic} {Environments}},\n\turl = {paper=https://ieeexplore.ieee.org/document/8793765/ video=https://www.youtube.com/watch?v=Kx14E98Iqng},\n\tdoi = {10.1109/ICRA.2019.8793765},\n\tbooktitle = {2019 {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\n\tpublisher = {IEEE},\n\tauthor = {Zhu, H. and Juhl, J. and Ferranti, L. and Alonso-Mora, J.},\n\tmonth = may,\n\tyear = {2019},\n\tnote = {Best paper award in Multi-Robot Systems},\n\tkeywords = {key\\_collision\\_avoidance, key\\_formation\\_control, key\\_motion\\_planning, key\\_multirobot, type\\_conference},\n\tpages = {9080--9086},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8392438/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019', 'https://ieeexplore.ieee.org/document/8392438/', event);\">\n    SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Pu, Y.; Jones, C. N.;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Automatic Control</i>, 64(5): 1800–1815. May 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8392438/\"\n     onclick=\"log_download('ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019', 'https://ieeexplore.ieee.org/document/8392438/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TAC.2018.2849566\"\n     onclick=\"log_download('ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019', 'http://doi.org/10.1109/TAC.2018.2849566', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-pu-jones-keviczky-svramaanasynchronousalternatingminimizationalgorithmwithvariancereductionformodelpredictivecontrolapplications-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ferranti_svr-ama_2019,\n\ttitle = {{SVR}-{AMA}: {An} {Asynchronous} {Alternating} {Minimization} {Algorithm} {With} {Variance} {Reduction} for {Model} {Predictive} {Control} {Applications}},\n\tvolume = {64},\n\tshorttitle = {{SVR}-{AMA}},\n\turl = {https://ieeexplore.ieee.org/document/8392438/},\n\tdoi = {10.1109/TAC.2018.2849566},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Automatic Control},\n\tauthor = {Ferranti, L. and Pu, Y. and Jones, C. N. and Keviczky, T.},\n\tmonth = may,\n\tyear = {2019},\n\tkeywords = {key\\_aerospace, key\\_mpc, key\\_splitting, type\\_journal},\n\tpages = {1800--1815},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8550178/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018', 'https://ieeexplore.ieee.org/document/8550178/', event);\">\n    Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Negenborn, R. R.; Keviczky, T.;  and Alonso-Mora, J.\n</span>\n\n  \n\n</span>\n\n  In <i>2018 European Control Conference (ECC)</i>, pages 2523–2528, Limassol, June 2018. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8550178/\"\n     onclick=\"log_download('ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018', 'https://ieeexplore.ieee.org/document/8550178/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.23919/ECC.2018.8550178\"\n     onclick=\"log_download('ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018', 'http://doi.org/10.23919/ECC.2018.8550178', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-negenborn-keviczky-alonsomora-coordinationofmultiplevesselsviadistributednonlinearmodelpredictivecontrol-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferranti_coordination_2018,\n\taddress = {Limassol},\n\ttitle = {Coordination of {Multiple} {Vessels} {Via} {Distributed} {Nonlinear} {Model} {Predictive} {Control}},\n\turl = {https://ieeexplore.ieee.org/document/8550178/},\n\tdoi = {10.23919/ECC.2018.8550178},\n\tbooktitle = {2018 {European} {Control} {Conference} ({ECC})},\n\tpublisher = {IEEE},\n\tauthor = {Ferranti, L. and Negenborn, R. R. and Keviczky, T. and Alonso-Mora, J.},\n\tmonth = jun,\n\tyear = {2018},\n\tkeywords = {key\\_collision\\_avoidance, key\\_maritime, key\\_motion\\_planning, key\\_multirobot, key\\_splitting, type\\_conference},\n\tpages = {2523--2528},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/1.G000288\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017', 'https://arc.aiaa.org/doi/10.2514/1.G000288', event);\">\n    Operator-Splitting and Gradient Methods for Real-Time Predictive Flight Control Design.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Guidance, Control, and Dynamics</i>, 40(2): 265–277. February 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/1.G000288\"\n     onclick=\"log_download('ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017', 'https://arc.aiaa.org/doi/10.2514/1.G000288', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Operator-Splitting and Gradient Methods for Real-Time Predictive Flight Control Design [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/1.G000288\"\n     onclick=\"log_download('ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017', 'http://doi.org/10.2514/1.G000288', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-keviczky-operatorsplittingandgradientmethodsforrealtimepredictiveflightcontroldesign-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ferranti_operator-splitting_2017,\n\ttitle = {Operator-{Splitting} and {Gradient} {Methods} for {Real}-{Time} {Predictive} {Flight} {Control} {Design}},\n\tvolume = {40},\n\tissn = {0731-5090, 1533-3884},\n\turl = {https://arc.aiaa.org/doi/10.2514/1.G000288},\n\tdoi = {10.2514/1.G000288},\n\tlanguage = {en},\n\tnumber = {2},\n\tjournal = {Journal of Guidance, Control, and Dynamics},\n\tauthor = {Ferranti, L. and Keviczky, T.},\n\tmonth = feb,\n\tyear = {2017},\n\tkeywords = {key\\_aerospace, key\\_mpc, key\\_splitting, type\\_journal},\n\tpages = {265--277},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-onlineoptimizationbasedpredictiveflightcontrolusingfirstordermethods-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-onlineoptimizationbasedpredictiveflightcontrolusingfirstordermethods-2017', 'http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba', event);\">\n    Online Optimization-Based Predictive Flight Control Using First-Order Methods.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, Delft University of Technology,  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba\"\n     onclick=\"log_download('ferranti-onlineoptimizationbasedpredictiveflightcontrolusingfirstordermethods-2017', 'http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Online Optimization-Based Predictive Flight Control Using First-Order Methods [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-onlineoptimizationbasedpredictiveflightcontrolusingfirstordermethods-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-onlineoptimizationbasedpredictiveflightcontrolusingfirstordermethods-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{ferranti_online_2017,\n\ttitle = {Online {Optimization}-{Based} {Predictive} {Flight} {Control} {Using} {First}-{Order} {Methods}},\n\turl = {http://resolver.tudelft.nl/uuid:786608c2-38ce-4dbc-97d2-8a26080829ba},\n\tschool = {Delft University of Technology},\n\tauthor = {Ferranti, L.},\n\tyear = {2017},\n\tkeywords = {key\\_aerospace, key\\_dual\\_gradient\\_descent, key\\_mpc, key\\_splitting},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ieeexplore.ieee.org/document/7798612/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016', 'http://ieeexplore.ieee.org/document/7798612/', event);\">\n    Constrained LQR using online decomposition techniques.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Stathopoulos, G.; Jones, C. N.;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  In <i>2016 IEEE 55th Conference on Decision and Control (CDC)</i>, pages 2339–2344, Las Vegas, NV, USA, December 2016. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ieeexplore.ieee.org/document/7798612/\"\n     onclick=\"log_download('ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016', 'http://ieeexplore.ieee.org/document/7798612/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Constrained LQR using online decomposition techniques [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/CDC.2016.7798612\"\n     onclick=\"log_download('ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016', 'http://doi.org/10.1109/CDC.2016.7798612', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-stathopoulos-jones-keviczky-constrainedlqrusingonlinedecompositiontechniques-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferranti_constrained_2016,\n\taddress = {Las Vegas, NV, USA},\n\ttitle = {Constrained {LQR} using online decomposition techniques},\n\turl = {http://ieeexplore.ieee.org/document/7798612/},\n\tdoi = {10.1109/CDC.2016.7798612},\n\tbooktitle = {2016 {IEEE} 55th {Conference} on {Decision} and {Control} ({CDC})},\n\tpublisher = {IEEE},\n\tauthor = {Ferranti, L. and Stathopoulos, G. and Jones, C. N. and Keviczky, T.},\n\tmonth = dec,\n\tyear = {2016},\n\tkeywords = {key\\_mpc, key\\_splitting, type\\_conference},\n\tpages = {2339--2344},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ieeexplore.ieee.org/document/7798613/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016', 'http://ieeexplore.ieee.org/document/7798613/', event);\">\n    Asynchronous splitting design for Model Predictive Control.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Pu, Y.; Jones, C. N.;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  In <i>2016 IEEE 55th Conference on Decision and Control (CDC)</i>, pages 2345–2350, Las Vegas, NV, USA, December 2016. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ieeexplore.ieee.org/document/7798613/\"\n     onclick=\"log_download('ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016', 'http://ieeexplore.ieee.org/document/7798613/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Asynchronous splitting design for Model Predictive Control [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/CDC.2016.7798613\"\n     onclick=\"log_download('ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016', 'http://doi.org/10.1109/CDC.2016.7798613', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-pu-jones-keviczky-asynchronoussplittingdesignformodelpredictivecontrol-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferranti_asynchronous_2016,\n\taddress = {Las Vegas, NV, USA},\n\ttitle = {Asynchronous splitting design for {Model} {Predictive} {Control}},\n\turl = {http://ieeexplore.ieee.org/document/7798613/},\n\tdoi = {10.1109/CDC.2016.7798613},\n\tbooktitle = {2016 {IEEE} 55th {Conference} on {Decision} and {Control} ({CDC})},\n\tpublisher = {IEEE},\n\tauthor = {Ferranti, L. and Pu, Y. and Jones, C. N. and Keviczky, T.},\n\tmonth = dec,\n\tyear = {2016},\n\tkeywords = {key\\_aerospace, key\\_mpc, key\\_splitting, type\\_conference},\n\tpages = {2345--2350},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ieeexplore.ieee.org/document/7810513/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016', 'http://ieeexplore.ieee.org/document/7810513/', event);\">\n    MPC design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  In <i>2016 European Control Conference (ECC)</i>, pages 1562–1567, Aalborg, Denmark, June 2016. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ieeexplore.ieee.org/document/7810513/\"\n     onclick=\"log_download('ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016', 'http://ieeexplore.ieee.org/document/7810513/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"MPC design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ECC.2016.7810513\"\n     onclick=\"log_download('ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016', 'http://doi.org/10.1109/ECC.2016.7810513', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-keviczky-mpcdesignforthelongitudinalmotionofapassengeraircraftbasedonoperatorsplittingandfastgradientmethods-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferranti_mpc_2016,\n\taddress = {Aalborg, Denmark},\n\ttitle = {{MPC} design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods},\n\turl = {http://ieeexplore.ieee.org/document/7810513/},\n\tdoi = {10.1109/ECC.2016.7810513},\n\tbooktitle = {2016 {European} {Control} {Conference} ({ECC})},\n\tpublisher = {IEEE},\n\tauthor = {Ferranti, Laura and Keviczky, Tamas},\n\tmonth = jun,\n\tyear = {2016},\n\tkeywords = {key\\_aerospace, key\\_dual\\_gradient\\_descent, key\\_mpc, type\\_conference},\n\tpages = {1562--1567},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ieeexplore.ieee.org/document/7402568/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015', 'http://ieeexplore.ieee.org/document/7402568/', event);\">\n    A parallel dual fast gradient method for MPC applications.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  In <i>2015 54th IEEE Conference on Decision and Control (CDC)</i>, pages 2406–2413, Osaka, December 2015. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ieeexplore.ieee.org/document/7402568/\"\n     onclick=\"log_download('ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015', 'http://ieeexplore.ieee.org/document/7402568/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A parallel dual fast gradient method for MPC applications [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/CDC.2015.7402568\"\n     onclick=\"log_download('ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015', 'http://doi.org/10.1109/CDC.2015.7402568', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-keviczky-aparalleldualfastgradientmethodformpcapplications-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferranti_parallel_2015,\n\taddress = {Osaka},\n\ttitle = {A parallel dual fast gradient method for {MPC} applications},\n\turl = {http://ieeexplore.ieee.org/document/7402568/},\n\tdoi = {10.1109/CDC.2015.7402568},\n\tbooktitle = {2015 54th {IEEE} {Conference} on {Decision} and {Control} ({CDC})},\n\tpublisher = {IEEE},\n\tauthor = {Ferranti, L. and Keviczky, T.},\n\tmonth = dec,\n\tyear = {2015},\n\tkeywords = {key\\_aerospace, key\\_control, key\\_mpc, key\\_splitting, type\\_conference},\n\tpages = {2406--2413},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/10.1002/oca.2121\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015', 'https://onlinelibrary.wiley.com/doi/10.1002/oca.2121', event);\">\n    An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Necoara, I.; <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  <i>Optimal Control Applications and Methods</i>, 36(5): 648–666. September 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/10.1002/oca.2121\"\n     onclick=\"log_download('necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015', 'https://onlinelibrary.wiley.com/doi/10.1002/oca.2121', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1002/oca.2121\"\n     onclick=\"log_download('necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015', 'http://doi.org/10.1002/oca.2121', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('necoara-ferranti-keviczky-anadaptiveconstrainttighteningapproachtolinearmodelpredictivecontrolbasedonapproximationalgorithmsforoptimization-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{necoara_adaptive_2015,\n\ttitle = {An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization},\n\tvolume = {36},\n\tissn = {01432087},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/oca.2121},\n\tdoi = {10.1002/oca.2121},\n\tnumber = {5},\n\tjournal = {Optimal Control Applications and Methods},\n\tauthor = {Necoara, Ion and Ferranti, Laura and Keviczky, Tamás},\n\tmonth = sep,\n\tyear = {2015},\n\tkeywords = {key\\_aerospace, key\\_dual\\_gradient\\_descent, key\\_mpc, type\\_journal},\n\tpages = {648--666},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015', 'https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628', event);\">\n    Predictive Flight Control with Active Diagnosis and Reconfiguration for Actuator Jamming.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://r2clab.com/\">Ferranti, L.</a>; Wan, Y.;  and Keviczky, T.\n</span>\n\n  \n\n</span>\n\n  <i>IFAC-PapersOnLine</i>, 48(23): 166–171.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628\"\n     onclick=\"log_download('ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015', 'https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Predictive Flight Control with Active Diagnosis and Reconfiguration for Actuator Jamming [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ifacol.2015.11.278\"\n     onclick=\"log_download('ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015', 'http://doi.org/10.1016/j.ifacol.2015.11.278', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferranti-wan-keviczky-predictiveflightcontrolwithactivediagnosisandreconfigurationforactuatorjamming-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ferranti_predictive_2015,\n\ttitle = {Predictive {Flight} {Control} with {Active} {Diagnosis} and {Reconfiguration} for {Actuator} {Jamming}},\n\tvolume = {48},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2405896315025628},\n\tdoi = {10.1016/j.ifacol.2015.11.278},\n\tlanguage = {en},\n\tnumber = {23},\n\tjournal = {IFAC-PapersOnLine},\n\tauthor = {Ferranti, L. and Wan, Y. and Keviczky, T.},\n\tyear = {2015},\n\tkeywords = {key\\_aerospace, key\\_fault\\_tolerant, key\\_mpc, type\\_conference},\n\tpages = {166--171},\n}\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);