DREAM: Decentralized Real-time Asynchronous Probabilistic Trajectory Planning for Collision-free Multi-Robot Navigation in Cluttered Environments
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Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multi-robot 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 multi-robot trajectory planning algorithm allowing inter-robot collision and static and dynamic obstacle avoidance. Our planner simulates the behavior of dynamic obstacles during decision-making, 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 re-orderings. 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 1.68x success rate using as low as 0.28x time in single-robot, and up to 2.15x success rate using as low as 0.36x time in multi-robot cases compared to the best baseline. We implement our planner on real quadrotors to show its real-world applicability.
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