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\n\n \n \n \n \n \n \n TuneNet: One-Shot Simulation Tuning for Physics Prediction and Robot Task Planning.\n \n \n \n \n\n\n \n Adam Allevato; Elaine Schaertl Short; Mitch Pryor; and Andrea Thomaz.\n\n\n \n\n\n\n In Osaka, Japan, October 2019. \n
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@inproceedings{allevato_tunenet_2019,\n\taddress = {Osaka, Japan},\n\ttitle = {{TuneNet}: {One}-{Shot} {Simulation} {Tuning} for {Physics} {Prediction} and {Robot} {Task} {Planning}},\n\tshorttitle = {{1G}-04},\n\turl = {http://proceedings.mlr.press/v100/allevato20a.html},\n\tauthor = {Allevato, Adam and Short, Elaine Schaertl and Pryor, Mitch and Thomaz, Andrea},\n\tmonth = oct,\n\tyear = {2019},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n A 3D obstacle detection system for a complex mobile robot in a hazardous underground tunnel environment.\n \n \n \n \n\n\n \n Christopher William Suarez.\n\n\n \n\n\n\n Master's thesis, May 2019.\n
Accepted: 2019-10-25T14:56:12Z\n\n
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@mastersthesis{suarez_3d_2019,\n\ttitle = {A {3D} obstacle detection system for a complex mobile robot in a hazardous underground tunnel environment},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/77434},\n\tabstract = {This thesis presents a 3D obstacle detection system developed for use in the H-Canyon Air Exhaust (HCAEX) tunnel project. The HCAEX tunnel is a harsh environment with positive, negative, and hanging obstacles along with a muddy uneven floor. The mobile platform developed to explore this tunnel is highly complex and requires advanced knowledge of its state relative to the environment. A LiDAR sensor was identified and a Robot Operating System (ROS) package was developed to detect obstacles in 3D while accounting for the challenges presented by the project. Tests were performed in two outdoor environments and an HCAEX mock tunnel environment. Results showed that the obstacle detection system correctly identified obstacles in the environments at both roll and pitch states up to 45°, though further refinement and implementation can be performed.},\n\tlanguage = {en},\n\turldate = {2020-05-08},\n\tauthor = {Suarez, Christopher William},\n\tmonth = may,\n\tyear = {2019},\n\tdoi = {http://dx.doi.org/10.26153/tsw/4523},\n\tdoi = {http://dx.doi.org/10.26153/tsw/4523},\n\tnote = {Accepted: 2019-10-25T14:56:12Z},\n}\n\n\n\n
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\n This thesis presents a 3D obstacle detection system developed for use in the H-Canyon Air Exhaust (HCAEX) tunnel project. The HCAEX tunnel is a harsh environment with positive, negative, and hanging obstacles along with a muddy uneven floor. The mobile platform developed to explore this tunnel is highly complex and requires advanced knowledge of its state relative to the environment. A LiDAR sensor was identified and a Robot Operating System (ROS) package was developed to detect obstacles in 3D while accounting for the challenges presented by the project. Tests were performed in two outdoor environments and an HCAEX mock tunnel environment. Results showed that the obstacle detection system correctly identified obstacles in the environments at both roll and pitch states up to 45°, though further refinement and implementation can be performed.\n
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\n\n \n \n \n \n \n Mobile Robotic Radiation Surveying Using Recursive Bayesian Estimation.\n \n \n \n\n\n \n Blake Anderson; Mitch Pryor; and Sheldon Landsberger.\n\n\n \n\n\n\n In
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pages 1187–1192, August 2019. \n
ISSN: 2161-8089\n\n
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@inproceedings{anderson_mobile_2019,\n\ttitle = {Mobile {Robotic} {Radiation} {Surveying} {Using} {Recursive} {Bayesian} {Estimation}},\n\tdoi = {http://doi.org/10.1109/COASE.2019.8843064},\n\tabstract = {Nuclear facilities require wide-area surveys and remote response to the detection of abnormal radiation levels. These typically require a large number of measurement locations using fixed search patterns. Such approaches are time-consuming, require extended radiation exposure, and are difficult to routinely replicate by technicians. This paper presents an automated method of detecting and locating single or multiple small gamma-ray sources in an unstructured environment, requiring significantly fewer measurements than traditional methods and without a need for post-processing. A mobile robot can collect higher-precision data than practically possible by a human and removes the technician from the radiation area. This is enabled by addressing complexities that previously made automation difficult including supervisory control, obstacle avoidance, sensor positioning over a large height range, recognizing environmental complexities (shielding, etc and modifying survey parameters based on aberrant readings. The developed solution uses a mobile platform with a height-adjustable (up to 2.44 meters) radiation detector. Recursive Bayesian Estimation (RBE) is used to update a probability distribution of the location and intensity of source(s) after each measurement. The likelihood function is determined using radiation transport and detector models. Isotopic identification via a gamma library search aids data analysis by distinguishing counts from different sources. Computation considerations are discussed including predicting and localizing multiple sources.},\n\tbooktitle = {2019 {IEEE} 15th {International} {Conference} on {Automation} {Science} and {Engineering} ({CASE})},\n\tauthor = {Anderson, Blake and Pryor, Mitch and Landsberger, Sheldon},\n\tmonth = aug,\n\tyear = {2019},\n\tnote = {ISSN: 2161-8089},\n\tkeywords = {Bayes methods, Detectors, Estimation, Hardware, Probability and Statistical Methods, Robot sensing systems, Robotics in Hazardous Fields, Uncertainty, abnormal radiation levels, automated method, collision avoidance, data analysis, detector models, environmental complexities, fixed search patterns, gamma library search, gamma-ray sources, height range, height-adjustable, higher-precision data, measurement locations, mobile platform, mobile robot, mobile robotic radiation surveying, mobile robots, nuclear facilities, probability, radiation area, radiation detection, radiation exposure, radiation transport, recursive Bayesian Estimation, recursive Bayesian estimation, remote response, search problems, supervisory control, survey parameters, wide-area surveys},\n\tpages = {1187--1192},\n}\n\n\n\n
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\n Nuclear facilities require wide-area surveys and remote response to the detection of abnormal radiation levels. These typically require a large number of measurement locations using fixed search patterns. Such approaches are time-consuming, require extended radiation exposure, and are difficult to routinely replicate by technicians. This paper presents an automated method of detecting and locating single or multiple small gamma-ray sources in an unstructured environment, requiring significantly fewer measurements than traditional methods and without a need for post-processing. A mobile robot can collect higher-precision data than practically possible by a human and removes the technician from the radiation area. This is enabled by addressing complexities that previously made automation difficult including supervisory control, obstacle avoidance, sensor positioning over a large height range, recognizing environmental complexities (shielding, etc and modifying survey parameters based on aberrant readings. The developed solution uses a mobile platform with a height-adjustable (up to 2.44 meters) radiation detector. Recursive Bayesian Estimation (RBE) is used to update a probability distribution of the location and intensity of source(s) after each measurement. The likelihood function is determined using radiation transport and detector models. Isotopic identification via a gamma library search aids data analysis by distinguishing counts from different sources. Computation considerations are discussed including predicting and localizing multiple sources.\n
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\n\n \n \n \n \n \n Obstacle Persistent Adaptive Map Maintenance for Autonomous Mobile Robots using Spatio-temporal Reasoning.\n \n \n \n\n\n \n Meredith L. Pitsch; and Mitchell W. Pryor.\n\n\n \n\n\n\n In
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pages 1023–1028, August 2019. \n
ISSN: 2161-8089\n\n
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@inproceedings{pitsch_obstacle_2019,\n\ttitle = {Obstacle {Persistent} {Adaptive} {Map} {Maintenance} for {Autonomous} {Mobile} {Robots} using {Spatio}-temporal {Reasoning}},\n\tdoi = {10.1109/COASE.2019.8843095},\n\tabstract = {Mobile robotic systems operate in increasingly realistic scenarios even as users have increased expectations for the duration of autonomous tasks. Mobile robots face unique challenges when operating in environments that change over time, where systems must maintain an accurate representation of the environment with respect to both spatial and temporal dimensions. This paper describes a spatio-temporal technique for extending the autonomy of a mobile robot in a changing environment. This new technique called Obstacle Persistent Adaptive Map Maintenance (OPAMM) uses navigation data collected during normal operations to perform periodic self-maintenance of its environment model. OPAMM implements a probabilistic feature persistence model to predict the survival state of obstacles and update the world model. Maintaining an accurate world model is necessary for extending the long-term autonomy of robots in realistic scenarios. Results show that robots using OPAMM had localizations scores higher than other methods, thus reducing long-term localization degradation.},\n\tbooktitle = {2019 {IEEE} 15th {International} {Conference} on {Automation} {Science} and {Engineering} ({CASE})},\n\tauthor = {Pitsch, Meredith L. and Pryor, Mitchell W.},\n\tmonth = aug,\n\tyear = {2019},\n\tnote = {ISSN: 2161-8089},\n\tkeywords = {Automation, Computer aided software engineering, Conferences, OPAMM, autonomous mobile robots, autonomous tasks, changing environment, environment model, long-term localization degradation, mobile robotic systems, mobile robots, obstacle persistent adaptive map maintenance, periodic self-maintenance, probabilistic feature persistence model, spatial dimensions, spatio-temporal reasoning, spatio-temporal technique, temporal dimensions, temporal reasoning},\n\tpages = {1023--1028},\n}\n\n\n\n
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\n Mobile robotic systems operate in increasingly realistic scenarios even as users have increased expectations for the duration of autonomous tasks. Mobile robots face unique challenges when operating in environments that change over time, where systems must maintain an accurate representation of the environment with respect to both spatial and temporal dimensions. This paper describes a spatio-temporal technique for extending the autonomy of a mobile robot in a changing environment. This new technique called Obstacle Persistent Adaptive Map Maintenance (OPAMM) uses navigation data collected during normal operations to perform periodic self-maintenance of its environment model. OPAMM implements a probabilistic feature persistence model to predict the survival state of obstacles and update the world model. Maintaining an accurate world model is necessary for extending the long-term autonomy of robots in realistic scenarios. Results show that robots using OPAMM had localizations scores higher than other methods, thus reducing long-term localization degradation.\n
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\n\n \n \n \n \n \n \n Perception pipeline for remote tunnel inspection rover.\n \n \n \n \n\n\n \n Conor Alexander McMahon.\n\n\n \n\n\n\n Ph.D. Thesis, May 2019.\n
Accepted: 2019-11-21T16:49:02Z\n\n
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@phdthesis{mcmahon_perception_2019,\n\ttype = {Thesis},\n\ttitle = {Perception pipeline for remote tunnel inspection rover},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/78529},\n\tabstract = {Architectural structures require routine structural inspections, and these checks are increasingly performed by remote robotic systems. Inspections often meet all of the traditional "three D's" of robotics - Dull, Dirty, and Dangerous - and may be highly suited to automation. Underground tunnel systems present a particularly important opportunity for robots because these systems may have environmental hazards which make human deployment untenable. Unfortunately, tunnels are also a challenging environment for robots because they feature high self-similarity which makes localization along the tunnel axis difficult, in particular for cases where rough terrain renders odometry information unreliable. \n \nHere a platform-agnostic pipeline is presented for tunnel mapping and inspection. This system features tools to automatically segment the various concrete surfaces of the tunnel and individually analyze them for damage. A novel feature-based registration routine is also presented to overcome the localization challenges inherent in tunnels. The pipeline was validated using a LiDAR-based sensor tree and its performance in terms of registration and depth mapping accuracy was extensively tested. The registration methods utilized here do not depend on odometry or the use of registration targets and were shown to outperform contemporary approaches which are standard in industry.},\n\tlanguage = {en},\n\turldate = {2020-05-10},\n\tauthor = {McMahon, Conor Alexander},\n\tmonth = may,\n\tyear = {2019},\n\tdoi = {http://dx.doi.org/10.26153/tsw/5585},\n\tdoi = {http://dx.doi.org/10.26153/tsw/5585},\n\tnote = {Accepted: 2019-11-21T16:49:02Z},\n}\n\n\n\n
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\n Architectural structures require routine structural inspections, and these checks are increasingly performed by remote robotic systems. Inspections often meet all of the traditional \"three D's\" of robotics - Dull, Dirty, and Dangerous - and may be highly suited to automation. Underground tunnel systems present a particularly important opportunity for robots because these systems may have environmental hazards which make human deployment untenable. Unfortunately, tunnels are also a challenging environment for robots because they feature high self-similarity which makes localization along the tunnel axis difficult, in particular for cases where rough terrain renders odometry information unreliable. Here a platform-agnostic pipeline is presented for tunnel mapping and inspection. This system features tools to automatically segment the various concrete surfaces of the tunnel and individually analyze them for damage. A novel feature-based registration routine is also presented to overcome the localization challenges inherent in tunnels. The pipeline was validated using a LiDAR-based sensor tree and its performance in terms of registration and depth mapping accuracy was extensively tested. The registration methods utilized here do not depend on odometry or the use of registration targets and were shown to outperform contemporary approaches which are standard in industry.\n
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\n\n \n \n \n \n \n Improving the strength of additively manufactured concrete structures via intra and inter-layer fiber reinforcement.\n \n \n \n\n\n \n Ademola Oridate; Oliver Uitz; Ali Aleem; Carolyn Seepersad; Mitch Pryor; and Patricia Clayton.\n\n\n \n\n\n\n August 2019.\n
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@misc{oridate_improving_2019,\n\taddress = {Austin, TX},\n\ttype = {Invited {Talk}},\n\ttitle = {Improving the strength of additively manufactured concrete structures via intra and inter-layer fiber reinforcement},\n\tabstract = {Additive manufacturing of concrete structures (via an extrusion-based process) enables the construction of structures with more complex geometries than a traditional (casting) process would allow. Engineers and designers can take advantage of this possibility to design structures with more appealing aesthetics or better optimized for strength, thermal insulation and other desired properties while minimizing material use and weight. However, this process poses a number of challenges due to layering effects. One such challenge is the compromise in strength of structures due to insufficient reinforcement and inter-layer adhesion. This research seeks to minimize this effect by exploring the use of fibers as inclusions in the concrete mix and as reinforcement between layers. Reinforced walls are tested using a four-point bending test and the results show a significant increase in strength compared to walls without reinforcement.},\n\tauthor = {Oridate, Ademola and Uitz, Oliver and Aleem, Ali and Seepersad, Carolyn and Pryor, Mitch and Clayton, Patricia},\n\tmonth = aug,\n\tyear = {2019},\n}\n\n\n\n
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\n Additive manufacturing of concrete structures (via an extrusion-based process) enables the construction of structures with more complex geometries than a traditional (casting) process would allow. Engineers and designers can take advantage of this possibility to design structures with more appealing aesthetics or better optimized for strength, thermal insulation and other desired properties while minimizing material use and weight. However, this process poses a number of challenges due to layering effects. One such challenge is the compromise in strength of structures due to insufficient reinforcement and inter-layer adhesion. This research seeks to minimize this effect by exploring the use of fibers as inclusions in the concrete mix and as reinforcement between layers. Reinforced walls are tested using a four-point bending test and the results show a significant increase in strength compared to walls without reinforcement.\n
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\n\n \n \n \n \n \n \n Learning A Human-Centered Representation of Robot Affordance Models.\n \n \n \n \n\n\n \n Adam Allevato; Elaine Short; Mitch Pryor; and Andrea Thomaz.\n\n\n \n\n\n\n June 2019.\n
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@misc{allevato_learning_2019,\n\taddress = {Freiburg, Germay},\n\ttype = {Poster},\n\ttitle = {Learning {A} {Human}-{Centered} {Representation} of {Robot} {Affordance} {Models}},\n\turl = {https://sites.google.com/view/rss19-learning-and-reasoning},\n\tauthor = {Allevato, Adam and Short, Elaine and Pryor, Mitch and Thomaz, Andrea},\n\tmonth = jun,\n\tyear = {2019},\n}\n\n\n\n\n\n\n\n
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\n\n \n \n \n \n \n \n Spatio-temporal map maintenance for extending autonomy in long-term mobile robotic tasks.\n \n \n \n \n\n\n \n Meredith Leeann Pitsch.\n\n\n \n\n\n\n Ph.D. Thesis, January 2019.\n
Accepted: 2019-02-01T20:40:23Z\n\n
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@phdthesis{pitsch_spatio-temporal_2019,\n\ttype = {Thesis},\n\ttitle = {Spatio-temporal map maintenance for extending autonomy in long-term mobile robotic tasks},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/72719},\n\tabstract = {Working in hazardous environments requires routine inspections in order to meet safety standards. Dangerous quantities of nuclear contamination can exist in infinitesimally small volumes. In order to confidently inspect a nuclear environment for radioactive sources, especially those which emit alpha radiation, technicians must carefully maintain detectors at a consistent velocity and distance from a source. Technicians must also take careful records of which areas have been surveyed or not are important so that no area is left unmonitored. This is a difficult, exhausting task when the coverage area is larger than an office space. An autonomous mobile robotic platform with Complete Coverage Path Planning (CCPP) can reduce dangerous exposure to humans and provide better information for Radiological Control Technicians (RCT). The developed robotic system - or RCTbot - is designed for long-term deployment with little human correction, intervention, or maintenance required. To do this, the RCTbot creates a map of the environment, continually updates it based on multiple sensor inputs, and searches its map for contamination. In nuclear environments, the areas of interest often remain spatially constant throughout the duration of an inspection and are considered temporally static. The RCTbot monitors temporally static environments but adapts to dynamic changes over time. It then uses its sensor data to update and maintain its map so no manual human intervention is necessary. The spatio-temporal map maintenance (STMM) is agnostic to the survey type, so the RCTbot system is viable for application domain other than nuclear.},\n\tlanguage = {en},\n\turldate = {2020-05-10},\n\tauthor = {Pitsch, Meredith Leeann},\n\tmonth = jan,\n\tyear = {2019},\n\tdoi = {10.15781/T2HD7PD3M},\n\tnote = {Accepted: 2019-02-01T20:40:23Z},\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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\n Working in hazardous environments requires routine inspections in order to meet safety standards. Dangerous quantities of nuclear contamination can exist in infinitesimally small volumes. In order to confidently inspect a nuclear environment for radioactive sources, especially those which emit alpha radiation, technicians must carefully maintain detectors at a consistent velocity and distance from a source. Technicians must also take careful records of which areas have been surveyed or not are important so that no area is left unmonitored. This is a difficult, exhausting task when the coverage area is larger than an office space. An autonomous mobile robotic platform with Complete Coverage Path Planning (CCPP) can reduce dangerous exposure to humans and provide better information for Radiological Control Technicians (RCT). The developed robotic system - or RCTbot - is designed for long-term deployment with little human correction, intervention, or maintenance required. To do this, the RCTbot creates a map of the environment, continually updates it based on multiple sensor inputs, and searches its map for contamination. In nuclear environments, the areas of interest often remain spatially constant throughout the duration of an inspection and are considered temporally static. The RCTbot monitors temporally static environments but adapts to dynamic changes over time. It then uses its sensor data to update and maintain its map so no manual human intervention is necessary. The spatio-temporal map maintenance (STMM) is agnostic to the survey type, so the RCTbot system is viable for application domain other than nuclear.\n
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\n\n \n \n \n \n \n \n Modular supervisory controller for complex systems.\n \n \n \n \n\n\n \n Melissa Mei Yun Lee.\n\n\n \n\n\n\n Ph.D. Thesis, February 2019.\n
Accepted: 2019-04-01T20:28:14Z\n\n
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@phdthesis{lee_modular_2019,\n\ttype = {Thesis},\n\ttitle = {Modular supervisory controller for complex systems},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/73907},\n\tabstract = {Automation for the oil and gas industry is driven by the need to improve efficiency, productivity, consistency, and personnel safety, while reducing cost. Fully automated systems alleviate the physical toll on human operators and allow them to focus on monitoring unsafe well events and machinery maintenance. Complex systems like drilling rigs and snubbing units require supervisory controllers that can safely coordinate equipment and processes, overcome interoperability challenges and allow for functional scalability without sacrificing safety, security, and consistency of operations. The primary objective of this report is to explore the feasibility of developing a modular supervisory controller architecture which addresses these concerns by modifying and extending existing architectures. Such modifications include the use of non-homogeneous models in sub-system modules, including discrete event models for control and physics-based models for collision avoidance, addition of a system compilation module (Meta Module) to identify simple design errors, and implementation of an algorithm for synthesis of modules and filters to replace missing sub-systems. This report discusses the implementation results of the modular supervisory control architecture (modMFSM) on a simplified two-machine drilling system for assessment of design practices. Simulations for three test cases were executed to assess the ability of the controller to correctly perform error-free operations, detect and react to possible collisions, and adapt to missing equipment. The report then discusses the possibilities of extending the modMFSM architecture to control large complex systems such as drilling rigs, using snubbing operations as an example.},\n\tlanguage = {en},\n\turldate = {2020-05-10},\n\tauthor = {Lee, Melissa Mei Yun},\n\tmonth = feb,\n\tyear = {2019},\n\tdoi = {http://dx.doi.org/10.26153/tsw/1039},\n\tdoi = {http://dx.doi.org/10.26153/tsw/1039},\n\tnote = {Accepted: 2019-04-01T20:28:14Z},\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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\n Automation for the oil and gas industry is driven by the need to improve efficiency, productivity, consistency, and personnel safety, while reducing cost. Fully automated systems alleviate the physical toll on human operators and allow them to focus on monitoring unsafe well events and machinery maintenance. Complex systems like drilling rigs and snubbing units require supervisory controllers that can safely coordinate equipment and processes, overcome interoperability challenges and allow for functional scalability without sacrificing safety, security, and consistency of operations. The primary objective of this report is to explore the feasibility of developing a modular supervisory controller architecture which addresses these concerns by modifying and extending existing architectures. Such modifications include the use of non-homogeneous models in sub-system modules, including discrete event models for control and physics-based models for collision avoidance, addition of a system compilation module (Meta Module) to identify simple design errors, and implementation of an algorithm for synthesis of modules and filters to replace missing sub-systems. This report discusses the implementation results of the modular supervisory control architecture (modMFSM) on a simplified two-machine drilling system for assessment of design practices. Simulations for three test cases were executed to assess the ability of the controller to correctly perform error-free operations, detect and react to possible collisions, and adapt to missing equipment. The report then discusses the possibilities of extending the modMFSM architecture to control large complex systems such as drilling rigs, using snubbing operations as an example.\n
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\n\n \n \n \n \n \n \n Reactive synthesis of action planners.\n \n \n \n \n\n\n \n Nitish Sharma.\n\n\n \n\n\n\n Ph.D. Thesis, February 2019.\n
Accepted: 2019-05-09T18:36:39Z\n\n
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@phdthesis{sharma_reactive_2019,\n\ttype = {Thesis},\n\ttitle = {Reactive synthesis of action planners},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/74526},\n\tabstract = {An increase in the level of autonomy marks one of the fundamental focuses of current robotic systems. This involves the ability of a robot to reason about its environment and plan its motion in order to carry out assigned tasks. For all tasks, it generally involves abstractions into discrete, logical actions, where each discrete action defines a particular capability of the robot. \n \nThe problem of synthesis of correct-by-construction action planners has been considered in this work. Action Description Language (ADL) is used to model the actions. These ADL definitions are then translated to Linear Temporal Logic (LTL). LTL based specifications are further used for the reactive synthesis of the strategy. \n \nThis work largely focuses on expressiveness which consists of a definition of the actions and system/environment behavior. Classical ADL semantics cannot handle multiple agents or non-determinism. A natural extension of ADL (referred to as ADLnE in this document) has been proposed which can handle dynamic environments, non-determinism, and multiple agents. \n \nThe proposed work can be seen as an extension to generic search based action planners. One such A* search-based method, Goal Oriented Action Planner (GOAP) has been considered which is based on ADL semantics and is limited by deterministic, single agent modeling. Through examples, it has been established that for deterministic, single agent and static (or at best quasi-static) systems, the proposed strategy matches that of GOAP. For dynamic and multi-agent situations, a reactive action plan is synthesized (if feasible) that is guaranteed to satisfy the formal specification, i.e. achieve the goal.},\n\tlanguage = {en},\n\turldate = {2020-05-10},\n\tauthor = {Sharma, Nitish},\n\tmonth = feb,\n\tyear = {2019},\n\tdoi = {http://dx.doi.org/10.26153/tsw/1646},\n\tdoi = {http://dx.doi.org/10.26153/tsw/1646},\n\tnote = {Accepted: 2019-05-09T18:36:39Z},\n}\n\n\n\n\n\n\n\n\n\n\n\n
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\n An increase in the level of autonomy marks one of the fundamental focuses of current robotic systems. This involves the ability of a robot to reason about its environment and plan its motion in order to carry out assigned tasks. For all tasks, it generally involves abstractions into discrete, logical actions, where each discrete action defines a particular capability of the robot. The problem of synthesis of correct-by-construction action planners has been considered in this work. Action Description Language (ADL) is used to model the actions. These ADL definitions are then translated to Linear Temporal Logic (LTL). LTL based specifications are further used for the reactive synthesis of the strategy. This work largely focuses on expressiveness which consists of a definition of the actions and system/environment behavior. Classical ADL semantics cannot handle multiple agents or non-determinism. A natural extension of ADL (referred to as ADLnE in this document) has been proposed which can handle dynamic environments, non-determinism, and multiple agents. The proposed work can be seen as an extension to generic search based action planners. One such A* search-based method, Goal Oriented Action Planner (GOAP) has been considered which is based on ADL semantics and is limited by deterministic, single agent modeling. Through examples, it has been established that for deterministic, single agent and static (or at best quasi-static) systems, the proposed strategy matches that of GOAP. For dynamic and multi-agent situations, a reactive action plan is synthesized (if feasible) that is guaranteed to satisfy the formal specification, i.e. achieve the goal.\n
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\n\n \n \n \n \n \n \n Task-Trajectory Analysis Package in the Robot Operating System.\n \n \n \n \n\n\n \n Christina Elisabeth Petlowany.\n\n\n \n\n\n\n Ph.D. Thesis, May 2019.\n
Accepted: 2019-11-20T23:58:11Z\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 22 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{petlowany_task-trajectory_2019,\n\ttype = {Thesis},\n\ttitle = {Task-{Trajectory} {Analysis} {Package} in the {Robot} {Operating} {System}},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/78518},\n\tabstract = {For many manufacturing tasks, such as welding and cutting, the task trajectory, or path, is known a priori in the object's reference frame. What is\nnot known is whether or not the robot can reach the entirety of the trajectory\ngiven the relative location of the object frame to the robot's base frame and its\nreachable and/or dexterous workspace. The problem increases in complexity with each additional object in the robot's workspace. Some robots need to perform tasks in cluttered or confined environments, such as a glovebox, and the ability to know if and where the manipulator can perform a certain task is crucial for both design and operation. This thesis describes the development, design, and implementation of a Task-Trajectory Analysis Package (T-TAP) within the Robot Operating System (ROS) framework.\n\nReachability has been extensively discussed in the literature, but current reachability visualization tools do not account for task data, and instead describe the robot's global workspace and thus take a long time to compute. Such tools may be useful for designing robotic systems, but their value diminishes when analyzing a specific task and environment. T-TAP focuses on the task space and is capable of producing real-time or near real-time feedback about the validity of a path. The results are shown in an easy-to-interpret visualization of the path points and their relative quality as measured using selected performance metrics.\n\nT-TAP contains several capabilities. The first, and simplest, validates\nreachability for discrete points along the trajectory. An inverse kinematic\n(IK) solver is used to plan from one trajectory point to the next. The user\ncan use standard ROS IK solvers or utilize their own IK solver. Next, T-TAP\nuses the Jacobian to analyze the system's performance as it completes the proposed trajectory. It ensures that joint and velocity limits are not violated, singularities are avoided, and is extensible to include additional user-defined performance metrics.\n\nT-TAP requires no prior computations, is hardware agnostic, and can\nbe run entirely in simulation. It can reduce the time required to place and\nplan a trajectory by an order of magnitude. It is designed to work seamlessly with existing ROS path-planning packages. The operator needs only to send the path to T-TAP and T-TAP will analyze the trajectory. This information will allow the operator to intelligently adjust the path so that it is reachable and viable.},\n\tlanguage = {en},\n\turldate = {2020-05-10},\n\tauthor = {Petlowany, Christina Elisabeth},\n\tmonth = may,\n\tyear = {2019},\n\tdoi = {http://dx.doi.org/10.26153/tsw/5579},\n\tdoi = {http://dx.doi.org/10.26153/tsw/5579},\n\tnote = {Accepted: 2019-11-20T23:58:11Z},\n}\n\n\n\n
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\n For many manufacturing tasks, such as welding and cutting, the task trajectory, or path, is known a priori in the object's reference frame. What is not known is whether or not the robot can reach the entirety of the trajectory given the relative location of the object frame to the robot's base frame and its reachable and/or dexterous workspace. The problem increases in complexity with each additional object in the robot's workspace. Some robots need to perform tasks in cluttered or confined environments, such as a glovebox, and the ability to know if and where the manipulator can perform a certain task is crucial for both design and operation. This thesis describes the development, design, and implementation of a Task-Trajectory Analysis Package (T-TAP) within the Robot Operating System (ROS) framework. Reachability has been extensively discussed in the literature, but current reachability visualization tools do not account for task data, and instead describe the robot's global workspace and thus take a long time to compute. Such tools may be useful for designing robotic systems, but their value diminishes when analyzing a specific task and environment. T-TAP focuses on the task space and is capable of producing real-time or near real-time feedback about the validity of a path. The results are shown in an easy-to-interpret visualization of the path points and their relative quality as measured using selected performance metrics. T-TAP contains several capabilities. The first, and simplest, validates reachability for discrete points along the trajectory. An inverse kinematic (IK) solver is used to plan from one trajectory point to the next. The user can use standard ROS IK solvers or utilize their own IK solver. Next, T-TAP uses the Jacobian to analyze the system's performance as it completes the proposed trajectory. It ensures that joint and velocity limits are not violated, singularities are avoided, and is extensible to include additional user-defined performance metrics. T-TAP requires no prior computations, is hardware agnostic, and can be run entirely in simulation. It can reduce the time required to place and plan a trajectory by an order of magnitude. It is designed to work seamlessly with existing ROS path-planning packages. The operator needs only to send the path to T-TAP and T-TAP will analyze the trajectory. This information will allow the operator to intelligently adjust the path so that it is reachable and viable.\n
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\n\n \n \n \n \n \n \n Instantaneous Center of Rotation-Based Master-Slave Kinematic Modeling and Control.\n \n \n \n \n\n\n \n Vikram Ramanathan; Andy Zelenak; and Mitch Pryor.\n\n\n \n\n\n\n In Park City, UT, October 2019. ASME\n
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@inproceedings{ramanathan_instantaneous_2019,\n\taddress = {Park City, UT},\n\ttitle = {Instantaneous {Center} of {Rotation}-{Based} {Master}-{Slave} {Kinematic} {Modeling} and {Control}},\n\turl = {https://asmedigitalcollection.asme.org/DSCC/proceedings/DSCC2019/59162/V003T17A005/1070613},\n\tdoi = {10.1115/DSCC2019-9123},\n\tlanguage = {en},\n\turldate = {2020-05-09},\n\tpublisher = {ASME},\n\tauthor = {Ramanathan, Vikram and Zelenak, Andy and Pryor, Mitch},\n\tmonth = oct,\n\tyear = {2019},\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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\n\n \n \n \n \n \n \n Virtual fixture generation for task planning with complex geometries.\n \n \n \n \n\n\n \n Andrew Patrick Sharp.\n\n\n \n\n\n\n Ph.D. Thesis, May 2019.\n
Accepted: 2019-12-10T00:48:10Z\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 22 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{sharp_virtual_2019,\n\ttype = {Dissertation},\n\ttitle = {Virtual fixture generation for task planning with complex geometries},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/78702},\n\tabstract = {Decontaminating and decommissioning aging nuclear facilities and managing nuclear waste required increased automation to reduce personnel dose. Semi-autonomous behaviors, such as virtual fixtures, aid task execution by managing low-level system resources while operators retain high-level control. Virtual fixtures provide operators with geometric constraints or guidance forces in a robotic manipulator’s workspace. This dissertation advances virtual fixture generation through, the previously unexplored, construction of layers of point cloud based Guidance Virtual Fixtures. Point clouds are used for virtual fixture generation, based on complex surface geometry, to provide more expressive, and therefore useful, environmental representations. Thus, this work builds upon previous point cloud based Forbidden Region Virtual Fixtures to address virtual fixture generation shortcomings outlined in the literature. Task input polygonal mesh checks warn operators if defects are found. Task normal vectors and task parameters are used to calculate point cloud layers at task defined distances from the surface. These layers are interpolated and voxelized to maintain point cloud resolution at increasing distances from the task surface. The layers are combined into a bi-directional graph structure for storage and future use. The graph structure is combined with a Forbidden Region Virtual Fixture to create a Task Virtual Fixture. \nTask Virtual Fixture generation was evaluated with multiple input types including parametric surfaces, polygonal meshes, and point cloud data. Results demonstrate surface model concavity affects the growth in the number of offset layer vertices as does distance from the task surface. Task Virtual Fixture generation intuitively modifies VF layer resolution at extended task surface distances. Point cloud sensor data demonstrated sensor data input for "open world" scenarios. Two visualization and task execution environments were developed to apply Task Virtual Fixtures to spatially discrete and spatially continuous non-contact tasks. The first interface, spatially discrete, was constructed with the Robot Operating System, RViz, and MoveIt!. This interface displays reachability information to the operator and is called the Manipulator to Task Transform Tool. The second interface allows operators to employ Task Virtual Fixture information in ABB’s RobotStudio for spatially continuous tasks. A small user study was conducted for each interface to demonstrate more expressive Task Virtual Fixtures are still operator interpretable and assist with task execution.},\n\tlanguage = {en},\n\turldate = {2020-05-09},\n\tauthor = {Sharp, Andrew Patrick},\n\tmonth = may,\n\tyear = {2019},\n\tdoi = {http://dx.doi.org/10.26153/tsw/5758},\n\tdoi = {http://dx.doi.org/10.26153/tsw/5758},\n\tnote = {Accepted: 2019-12-10T00:48:10Z},\n}\n\n\n\n
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\n Decontaminating and decommissioning aging nuclear facilities and managing nuclear waste required increased automation to reduce personnel dose. Semi-autonomous behaviors, such as virtual fixtures, aid task execution by managing low-level system resources while operators retain high-level control. Virtual fixtures provide operators with geometric constraints or guidance forces in a robotic manipulator’s workspace. This dissertation advances virtual fixture generation through, the previously unexplored, construction of layers of point cloud based Guidance Virtual Fixtures. Point clouds are used for virtual fixture generation, based on complex surface geometry, to provide more expressive, and therefore useful, environmental representations. Thus, this work builds upon previous point cloud based Forbidden Region Virtual Fixtures to address virtual fixture generation shortcomings outlined in the literature. Task input polygonal mesh checks warn operators if defects are found. Task normal vectors and task parameters are used to calculate point cloud layers at task defined distances from the surface. These layers are interpolated and voxelized to maintain point cloud resolution at increasing distances from the task surface. The layers are combined into a bi-directional graph structure for storage and future use. The graph structure is combined with a Forbidden Region Virtual Fixture to create a Task Virtual Fixture. Task Virtual Fixture generation was evaluated with multiple input types including parametric surfaces, polygonal meshes, and point cloud data. Results demonstrate surface model concavity affects the growth in the number of offset layer vertices as does distance from the task surface. Task Virtual Fixture generation intuitively modifies VF layer resolution at extended task surface distances. Point cloud sensor data demonstrated sensor data input for \"open world\" scenarios. Two visualization and task execution environments were developed to apply Task Virtual Fixtures to spatially discrete and spatially continuous non-contact tasks. The first interface, spatially discrete, was constructed with the Robot Operating System, RViz, and MoveIt!. This interface displays reachability information to the operator and is called the Manipulator to Task Transform Tool. The second interface allows operators to employ Task Virtual Fixture information in ABB’s RobotStudio for spatially continuous tasks. A small user study was conducted for each interface to demonstrate more expressive Task Virtual Fixtures are still operator interpretable and assist with task execution.\n
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