Comparison of control and cooperation frameworks for blended autonomy

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Provan, Gregory
Sohége, Yves
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Institute of Electrical and Electronics Engineers (IEEE)
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Autonomous vehicles, e.g., cars, aircraft or ships, will need to accept some degree of human control for the coming years. Consequently, a method of controlling autonomous systems (ASs) that integrates control inputs from humans and machines is critical. We describe a framework for blended autonomy, in which humans and ASs interact with varying degrees of control to safely achieve a task. We empirically compare collaborative control tasks in which the human and AS have identical or conflicting objectives, under three main control frameworks: (1) leader-follower control (based on Stackelberg games); (2) blended control; and (3) switching control. We validate our results on a car steering control model, given communication delays, noise and different collaboration levels.
Automobiles , Game theory , Mobile robots , Multi-robot systems , Steering systems , Blended autonomy , Autonomous vehicles , Human control , Autonomous systems , Control inputs , Collaborative control tasks , Blended control , Car steering control model , AS , Switching control , Leader-follower control , Stackelberg games , Task analysis , Linear programming , Collaboration , Mathematical model , Optimal control
Provan, G. and Sohége, Y. (2018) 'Comparison of Control and Cooperation Frameworks for Blended Autonomy'. 2018 European Control Conference (ECC), Limassol, Cyprus, 12 - 15 June. doi: 10.23919/ECC.2018.8550055
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