Online motion planning for multi-robot interaction using composable reachable sets.
This paper presents an algorithm for autonomous online path planning in uncertain, possibly adversarial, and partially observable environments. In contrast to many state-of-the-art motion planning approaches, our focus is on decision making in the presence of adversarial agents who may be acting strategically but whose exact behaviour is difficult to model precisely. Our algorithm first computes a collection of reachable sets with respect to a family of possible strategies available to the adversary. Online, the agent uses these sets as composable behavioural templates, in conjunction with a particle filter to maintain the current belief on the adversary's strategy. In partially observable environments, this yields significant performance improvements over state-of-the-art planning algorithms. We present empirical results to this effect using a robotic soccer simulator, highlighting the applicability of our implementation against adversaries with varying capabilities. We also demonstrate experiments on the NAO humanoid robots, in the context of different collision-avoidance scenarios.