Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Distributed Safe Reinforcement Learning for Multi-Robot Motion Planning
AU - Lu, Yang
AU - Guo, Yaohua
AU - Zhao, Guoxiang
AU - Zhu, Minghui
PY - 2021/7/15
Y1 - 2021/7/15
N2 - 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.
AB - 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.
U2 - 10.1109/MED51440.2021.9480176
DO - 10.1109/MED51440.2021.9480176
M3 - Conference contribution/Paper
SN - 9781665446600
T3 - 2021 29th Mediterranean Conference on Control and Automation (MED)
BT - 2021 29th Mediterranean Conference on Control and Automation (MED)
PB - IEEE
ER -