Barrier Function-based Safe Reinforcement Learning for Formation Control of Mobile Robots

Barrier Function-based Safe Reinforcement Learning for Formation Control of Mobile Robots

Zhang, Xinglong (National University of Defense Technology)
Peng, Yaoqian (National University of Defense Technology)
Pan, W. (TU Delft Robot Dynamics)
Xu, Xin (National University of Defense Technology)
Xie, Haibin (National University of Defense Technology)

Pappas, George J. (editor)
Kumar, Vijay (editor)

2022

Distributed model predictive control (DMPC) concerns how to online control multiple robotic systems with constraints effectively. However, the nonlinearity, nonconvexity, and strong interconnections of dynamic system models and constraints can make the real-time and real-world DMPC implementations nontrivial. Reinforcement learning (RL) algorithms are promising for control policy design. However, how to ensure safety in terms of state constraints in RL remains a significant issue. This paper proposes a barrier function-based safe reinforcement learning algorithm for DMPC of nonlinear multi-robot systems under state constraints. The proposed approach is composed of several local learning-based MPC regulators. Each regulator, associated with a local system, learns and deploys the local control policy using a safe reinforcement learning algorithm in a distributed manner, i.e., with state information only among the neighbor agents. As a prominent feature of the proposed algorithm, we present a novel barrier-based policy structure to ensure safety, which has a clear mechanistic interpretation. Both simulated and real-world experiments on the formation control of mobile robots with collision avoidance show the effectiveness of the proposed safe reinforcement learning algorithm for DMPC.

Regulators
Heuristic algorithms
Reinforcement learning
Prediction algorithms
Formation control
Safety
Mobile robots

http://resolver.tudelft.nl/uuid:f4e1f94d-0ec8-4f4a-82be-07b1cd2385e2

https://doi.org/10.1109/ICRA46639.2022.9811604

IEEE

2023-07-01

978-1-7281-9680-0

Proceedings of the International Conference on Robotics and Automation (ICRA 2022)

2022 International Conference on Robotics and Automation (ICRA), 2022-05-23 → 2022-05-27, Philadelphia, United States

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 Xinglong Zhang, Yaoqian Peng, W. Pan, Xin Xu, Haibin Xie