Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

TL;DR

This paper introduces a decentralized deep reinforcement learning approach for multi-robot collision avoidance that directly maps sensor data to movement commands, achieving scalable, efficient, and generalizable performance in complex environments.

Contribution

It proposes a novel sensor-level policy trained via multi-scenario reinforcement learning, reducing reliance on computationally intensive features and narrowing the performance gap with centralized methods.

Findings

Successfully navigates large robot teams with collision-free, time-efficient paths

Demonstrates strong generalization to unseen scenarios and heterogeneous robot groups

Achieves scalable performance in complex, large-scale environments

Abstract

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca