Abstract

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with the 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 to their centralized counterparts in practice.

In this paper, 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 avoidance 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 sim-to-real modeling error.

Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution to the 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.

BibTex

@article{fan2020distributed,

title={Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios},

  author={Fan, Tingxiang and Long, Pinxin and Liu, Wenxi and Pan, Jia},

journal={The International Journal of Robotics Research},

  year={2020}

}

Related Work

• Pinxin Long*, Tingxiang Fan*, Xinyi Liao, Wenxi Liu, Hao Zhang, Jia Pan. Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning. International Conference on Robotics and Automation (ICRA), 2018.
• Pinxin Long, Wenxi Liu, Jia Pan. Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation. IEEE Robotics and Automation Letters, 2(2), 656-663, 2017.

Acknowledgements

We would like to thank Hao Zhang from Dorabot Inc. and Ruigang Yang from Baidu Inc. for their support to us when preparing the physical robot experiments. We would like to thank Dinesh Manocha from the University of Maryland for his constructive discussion about the method and the experiments.