Comparison of control and cooperation frameworks for blended autonomy
Institute of Electrical and Electronics Engineers (IEEE)
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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