Multi-robot task allocation for safe planning against stochastic hazard dynamics

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in safety-critical exploration, surveillance, and emergency rescue missions. The multi-robot optimal control problem is challenging because of the dynamic uncertainties and the exponentially increasing problem size with the number of robots. Leveraging recent works obtaining a tractable safety maximizing plan for a single robot, we propose a scalable two-stage framework. Specifically, the problem is split into a low-level single-agent problem and a high-level task allocation problem. The low-level problem uses an efficient approximation of stochastic reachability for a Markov decision process to derive the optimal control policy under dynamic uncertainty. The task allocation is solved using forward and reverse greedy heuristics and in a distributed auction-based manner. Properties of our safety objective enable provable performance bounds on the safety of the approximate solutions of the two heuristics.