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\n\n \n \n \n \n \n MoBAr: a Hierarchical Action-Oriented Autonomous Control Architecture.\n \n \n \n\n\n \n Muñoz, P.; R-Moreno, M., D.; Barrero, D., F.; and Ropero, F.\n\n\n \n\n\n\n 2018.\n
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@misc{\n title = {MoBAr: a Hierarchical Action-Oriented Autonomous Control Architecture},\n type = {misc},\n year = {2018},\n source = {Journal of Intelligent and Robotic Systems: Theory and Applications},\n keywords = {Autonomous control,Autonomous exploration,Planning & execution,Planning & scheduling,Robotics},\n pages = {1-16},\n id = {5d78d02d-f774-371d-98b0-99fce2f4e75d},\n created = {2018-04-05T14:37:31.066Z},\n file_attached = {false},\n profile_id = {7bb64581-3780-374c-9752-deb713921fa6},\n last_modified = {2019-03-17T19:30:23.542Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2018 Springer Science+Business Media B.V., part of Springer Nature Autonomous control in robotics hold promising solutions for a broad number of applications. However, autonomous controllers require highly expertise on heterogeneous technologies, such as Artificial Intelligence Planning & Scheduling, behaviour modelling, intelligent execution and the hardware to control. Connecting these technologies entails several challenges to properly synchronize and verify the robot behaviours to deal with real scenarios. In this article, we present an autonomous controller based on high level modelling to easily enable adaptation of the controller to different robotics platforms and application domains. This controller, called MoBAr, allows on-board planning and replanning for goal oriented autonomy. It relies on technologies such as PLEXIL to model the execution behaviours, or the action oriented planning language PDDL for the domain definition and the planning process. Based on these technologies MoBAr enables an easier deployment of the autonomous controller for different robotics platforms. Moreover, MoBAr enables researching in planning systems applied to robotics domains, as it is possible to replace the PDDL planner and/or domain used without much effort. This fact is demonstrated in the experimental section, in which we demonstrate the adaptability and effectiveness of the controller in three different scenarios, i.e., a robotic arm, an office surveillance robot and an exploration rover while exploiting different planning systems.},\n bibtype = {misc},\n author = {Muñoz, Pablo and R-Moreno, María D. and Barrero, David F. and Ropero, Fernando},\n doi = {10.1007/s10846-018-0810-z}\n}
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\n © 2018 Springer Science+Business Media B.V., part of Springer Nature Autonomous control in robotics hold promising solutions for a broad number of applications. However, autonomous controllers require highly expertise on heterogeneous technologies, such as Artificial Intelligence Planning & Scheduling, behaviour modelling, intelligent execution and the hardware to control. Connecting these technologies entails several challenges to properly synchronize and verify the robot behaviours to deal with real scenarios. In this article, we present an autonomous controller based on high level modelling to easily enable adaptation of the controller to different robotics platforms and application domains. This controller, called MoBAr, allows on-board planning and replanning for goal oriented autonomy. It relies on technologies such as PLEXIL to model the execution behaviours, or the action oriented planning language PDDL for the domain definition and the planning process. Based on these technologies MoBAr enables an easier deployment of the autonomous controller for different robotics platforms. Moreover, MoBAr enables researching in planning systems applied to robotics domains, as it is possible to replace the PDDL planner and/or domain used without much effort. This fact is demonstrated in the experimental section, in which we demonstrate the adaptability and effectiveness of the controller in three different scenarios, i.e., a robotic arm, an office surveillance robot and an exploration rover while exploiting different planning systems.\n
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\n\n \n \n \n \n \n \n A strategical path planner for UGV-UAV cooperation in Mars Terrains.\n \n \n \n \n\n\n \n Ropero, F.; Muñoz, P.; and R-Moreno, M.\n\n\n \n\n\n\n Volume 11311 LNAI 2018.\n
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@book{\n title = {A strategical path planner for UGV-UAV cooperation in Mars Terrains},\n type = {book},\n year = {2018},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {Distributed AI algorithms,Intelligent agents,Planning and scheduling,Systems and applications},\n pages = {106-118},\n volume = {11311 LNAI},\n websites = {http://link.springer.com/10.1007/978-3-030-04191-5_8},\n id = {279d5de6-835d-3068-bab3-4be7f3618030},\n created = {2019-01-14T11:17:41.300Z},\n file_attached = {false},\n profile_id = {7bb64581-3780-374c-9752-deb713921fa6},\n last_modified = {2019-04-01T08:52:25.051Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© Springer Nature Switzerland AG 2018. Mars exploration is an ongoing researching topic mainly due to the technological breakthroughs in robotic platforms. Space agencies as NASA, are considering future Mars explorations where multi-robot teams cooperate to maximize the scientific return. In this regard, we present a cooperative team formed by a Unmanned Aerial Vehicle (UAV) and a Unmanned Ground Vehicle (UGV) to autonomously perform a Mars exploration. We develop a strategical path planner to compute a route plan for the UGV-UAV team to reach all the target points of the exploration. The key problems that we have considered in Mars explorations for the UGV-UAV team are: the UAV energy constraints and the UGV functionality constraints. Our strategical path planner models the UGV as a moving charging station which will carry the UAV through secure locations close to the target points locations, and the UAV will visit the target points using the UGV as a recharging station. Our solution has been tested in several scenarios and the results demonstrate that our approach is able to carry out a coordinated plan in a local optimal mission time on a real Mars terrain.},\n bibtype = {book},\n author = {Ropero, Fernando and Muñoz, Pablo and R-Moreno, M.D.},\n doi = {10.1007/978-3-030-04191-5_8}\n}
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\n © Springer Nature Switzerland AG 2018. Mars exploration is an ongoing researching topic mainly due to the technological breakthroughs in robotic platforms. Space agencies as NASA, are considering future Mars explorations where multi-robot teams cooperate to maximize the scientific return. In this regard, we present a cooperative team formed by a Unmanned Aerial Vehicle (UAV) and a Unmanned Ground Vehicle (UGV) to autonomously perform a Mars exploration. We develop a strategical path planner to compute a route plan for the UGV-UAV team to reach all the target points of the exploration. The key problems that we have considered in Mars explorations for the UGV-UAV team are: the UAV energy constraints and the UGV functionality constraints. Our strategical path planner models the UGV as a moving charging station which will carry the UAV through secure locations close to the target points locations, and the UAV will visit the target points using the UGV as a recharging station. Our solution has been tested in several scenarios and the results demonstrate that our approach is able to carry out a coordinated plan in a local optimal mission time on a real Mars terrain.\n
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\n\n \n \n \n \n \n \n A versatile executive based on T-REX for any robotic domain.\n \n \n \n \n\n\n \n Ropero, F.; Muñoz, P.; and R-Moreno, M., D.\n\n\n \n\n\n\n In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 11311 LNAI, pages 79-91, 2018. \n
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@inproceedings{\n title = {A versatile executive based on T-REX for any robotic domain},\n type = {inproceedings},\n year = {2018},\n keywords = {Distributed AI algorithms,Intelligent agents,Planning and scheduling},\n pages = {79-91},\n volume = {11311 LNAI},\n websites = {http://link.springer.com/10.1007/978-3-030-04191-5_6},\n id = {e5de5392-e8a6-3ac1-ad9d-9e4912893e47},\n created = {2019-01-14T11:18:00.787Z},\n file_attached = {false},\n profile_id = {7bb64581-3780-374c-9752-deb713921fa6},\n last_modified = {2019-04-01T08:52:25.050Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© Springer Nature Switzerland AG 2018. Autonomous controllers are highly expertise entities that integrate the sensing-planning-act cycle to operate robotic platforms in unaffordable environments. Its complexity usually makes them to be focused on a single robotic platform which is ostensibly inefficient. The Teleo-Reactive EXecutive (T-REX) is an autonomous controller envisaged as a multi-agent architecture where sensing, planning and execution are interleaved on a single agent. In this paper, we present a T-REX executive module to manage the execution cycle of actions during the planning phase. Our executive module, called GER, aims to state generic execution policies which make a T-REX controller turns into a heterogeneous entity able to operate over any robotic domain. The experimental section demonstrates that GER allows current T-REX architectures, such as GOAC, to manage different robotic domains as Unmanned Aerial Vehicles (UAV) or Unmanned Ground Vehicles (UGV).},\n bibtype = {inproceedings},\n author = {Ropero, Fernando and Muñoz, Pablo and R-Moreno, María D.},\n doi = {10.1007/978-3-030-04191-5_6},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}
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\n © Springer Nature Switzerland AG 2018. Autonomous controllers are highly expertise entities that integrate the sensing-planning-act cycle to operate robotic platforms in unaffordable environments. Its complexity usually makes them to be focused on a single robotic platform which is ostensibly inefficient. The Teleo-Reactive EXecutive (T-REX) is an autonomous controller envisaged as a multi-agent architecture where sensing, planning and execution are interleaved on a single agent. In this paper, we present a T-REX executive module to manage the execution cycle of actions during the planning phase. Our executive module, called GER, aims to state generic execution policies which make a T-REX controller turns into a heterogeneous entity able to operate over any robotic domain. The experimental section demonstrates that GER allows current T-REX architectures, such as GOAC, to manage different robotic domains as Unmanned Aerial Vehicles (UAV) or Unmanned Ground Vehicles (UGV).\n
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