  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 also describe a vision-based pipeline that allows
each player to estimate its opponent's relative position. We demonstrate that
our solution effectively competes against alternative strategies in a large
number of drone racing simulations. Hardware experiments with onboard vision
sensing prove the practicality of our strategy.
