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A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing


Spica, Riccardp; Falanga, Davide; Cristofalo, Erik; Montijano, Eduardo; Scaramuzza, Davide; Schwager, Mac (2018). A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing. In: Robotics: Science and Systems, Pittsburgh, 1 June 2018 - 6 June 2018.

Abstract

To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. Since unveiling one’s own strategy to the adversaries is not desirable, this requires each player to independently predict the other players’ future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations.

Abstract

To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. Since unveiling one’s own strategy to the adversaries is not desirable, this requires each player to independently predict the other players’ future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:6 June 2018
Deposited On:30 Oct 2019 15:15
Last Modified:30 Oct 2019 20:30
Publisher:s.n.
OA Status:Hybrid
Publisher DOI:https://doi.org/10.15607/rss.2018.xiv.040
Official URL:http://rpg.ifi.uzh.ch/docs/RSS18_Spica.pdf
Other Identification Number:merlin-id:18688

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