Distributed Safe Reinforcement Learning for Multi-Robot Motion Planning

Lu, Yang and Guo, Yaohua and Zhao, Guoxiang and Zhu, Minghui (2021) Distributed Safe Reinforcement Learning for Multi-Robot Motion Planning. In: 2021 29th Mediterranean Conference on Control and Automation (MED). 2021 29th Mediterranean Conference on Control and Automation (MED) . IEEE. ISBN 9781665446600

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Abstract

This paper studies optimal motion planning of multiple mobile robots with collision avoidance. We develop a distributed reinforcement learning algorithm which ensures suboptimal goal reaching and anytime collision avoidance simultaneously. Theoretical results on the convergence of neural network weights, the uniform and ultimate boundedness of system states of the closed-loop system, and anytime collision avoidance are established. Numerical simulations for single integrator and unicycle robots illustrate the effectiveness of our theoretical results.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
172571
Deposited By:
Deposited On:
16 Nov 2022 12:20
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Nov 2022 02:50