Decentralized, Safe, Multiagent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning

We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.