DREAM: Decentralized Real-Time Asynchronous Probabilistic Trajectory Planning for Collision-Free Multirobot Navigation in Cluttered Environments

Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multirobot tasks. Dynamic obstacles may also be interactive, i.e., their behavior varies based on the behavior of other entities. We propose a novel representation for interactive behavior of dynamic obstacles and a decentralized real-time multirobot trajectory planning algorithm allowing interrobot collision avoidance as well as static and dynamic obstacle avoidance. Our planner simulates the behavior of dynamic obstacles, accounting for interactivity. We account for the perception inaccuracy of static and prediction inaccuracy of dynamic obstacles. We handle asynchronous planning between teammates and message delays, drops, and reorderings. We evaluate our algorithm in simulations using 25400 random cases and compare it against three state-of-the-art baselines using 2100 random cases. Our algorithm achieves up to 1.68× success rate using as low as 0.28× time in single-robot, and up to 2.15× success rate using as low as 0.36× time in multirobot cases compared to the best baseline. We implement our planner on real quadrotors to show its real-world applicability.