Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/598490
Title: Synthesis of Human-in-the-Loop Control Protocols for Autonomous Systems
Authors: Lu Feng;Clemens Wiltsche;Laura Humphrey;Ufuk Topcu
subject: Control protocol synthesis|unmanned aerial vehicles (UAVs)|human factors|human–automation interaction|probabilistic models and specifications
Year: 2016
Publisher: IEEE
Abstract: We propose an approach to synthesize control protocols for autonomous systems that account for uncertainties and imperfections in interactions with human operators. As an illustrative example, we consider a scenario involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a human operator but also has a certain degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge about the uncertainties and imperfections in the human-automation interactions, we use abstractions based on Markov decision processes and augment these models to stochastic two-player games. Our approach enables the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the effects of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance. It can also provide informative feedback (e.g., Pareto curves showing the tradeoffs between multiple mission objectives), potentially assisting the operator in decision-making. We demonstrate the applicability of our approach via a detailed UAV mission planning case study.
URI: http://localhost/handle/Hannan/185684
http://localhost/handle/Hannan/598490
ISSN: 1545-5955
1558-3783
volume: 13
issue: 2
Appears in Collections:2016

Files in This Item:
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Title: Synthesis of Human-in-the-Loop Control Protocols for Autonomous Systems
Authors: Lu Feng;Clemens Wiltsche;Laura Humphrey;Ufuk Topcu
subject: Control protocol synthesis|unmanned aerial vehicles (UAVs)|human factors|human–automation interaction|probabilistic models and specifications
Year: 2016
Publisher: IEEE
Abstract: We propose an approach to synthesize control protocols for autonomous systems that account for uncertainties and imperfections in interactions with human operators. As an illustrative example, we consider a scenario involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a human operator but also has a certain degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge about the uncertainties and imperfections in the human-automation interactions, we use abstractions based on Markov decision processes and augment these models to stochastic two-player games. Our approach enables the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the effects of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance. It can also provide informative feedback (e.g., Pareto curves showing the tradeoffs between multiple mission objectives), potentially assisting the operator in decision-making. We demonstrate the applicability of our approach via a detailed UAV mission planning case study.
URI: http://localhost/handle/Hannan/185684
http://localhost/handle/Hannan/598490
ISSN: 1545-5955
1558-3783
volume: 13
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7428972.pdf1.83 MBAdobe PDFThumbnail
Preview File
Title: Synthesis of Human-in-the-Loop Control Protocols for Autonomous Systems
Authors: Lu Feng;Clemens Wiltsche;Laura Humphrey;Ufuk Topcu
subject: Control protocol synthesis|unmanned aerial vehicles (UAVs)|human factors|human–automation interaction|probabilistic models and specifications
Year: 2016
Publisher: IEEE
Abstract: We propose an approach to synthesize control protocols for autonomous systems that account for uncertainties and imperfections in interactions with human operators. As an illustrative example, we consider a scenario involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a human operator but also has a certain degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge about the uncertainties and imperfections in the human-automation interactions, we use abstractions based on Markov decision processes and augment these models to stochastic two-player games. Our approach enables the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the effects of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance. It can also provide informative feedback (e.g., Pareto curves showing the tradeoffs between multiple mission objectives), potentially assisting the operator in decision-making. We demonstrate the applicability of our approach via a detailed UAV mission planning case study.
URI: http://localhost/handle/Hannan/185684
http://localhost/handle/Hannan/598490
ISSN: 1545-5955
1558-3783
volume: 13
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7428972.pdf1.83 MBAdobe PDFThumbnail
Preview File