A Game Theoretic Approach to Autonomous Two-Player Drone Racing
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To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also avoid collisions with the opponents. Since unveiling one's own strategy to the adversaries in 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. The planner only requires that each player knows its own position (e.g. with GPS or SLAM), and can sense the other player's relative position (e.g. with on board vision). Our solution is demonstrated to effectively compete against alternative strategies in a large number of drone racing simulations. Hardware experiments with onboard vision sensing prove the practicality of our strategy.
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