Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios

By Tingxiang Fan, Pinxin Long, Wenxi Liu, Jia Pan. Int. J. Robotics Res., 2020

351 citations recorded by the source corpus.

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots’ states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent’s steering commands in terms of the 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 learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google.com/view/hybridmrca.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3c7a22a6e60a8adbfff34bc55cb07f6429b9e522

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Citations in the corpus

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2024, background): “• Manipulation subproblems where tasks can be enumerated a priori—e.g., grasping, in-hand manipulation, assembly—allow for zero-shot sim-to-real transfer, facilitating many of the real-world successes.”
Deep reinforcement learning based mobile robot navigation: A review (2021, methodology): “Therefore, they combined the classic PID controller with the DRL policy and proposed a hybrid-RL framework [61, 62] , classiﬁed the scenarios faced by a robot into three categories, and designed separate control sub-policies for each category.”