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 project, 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 the players positions which can be obtained by exploiting onboard GPS (for the ego robot) and a vision-based estimation algorithm (for the opponent). Our solution effectively competes against alternative strategies in a large number of drone racing simulations and can be implemented on real hardware.
Results and videos on multiple (an arbitrary number of) robots can be viewed on the following webpage.
R. Spica, D. Falanga, E. Cristofalo, E. Montijano, D. Scaramuzza, and M. Schwager, "A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing", Robotics: Science and Systems, 2018
Z. Wang, R. Spica, and M. Schwager, "Game Theoretic Motion Planning for Multi-Robot Racing", Proc. of the International Symposium on Distributed Autonomous Robotics Systems (DARS), 2018