中文版 | English
CSpace
Non-Communication Decentralized Multi-Robot Collision Avoidance in Grid Map Workspace with Double Deep Q-Network
Chen, Lin1,2; Zhao, Yongting1; Zhao, Huanjun1,2; Zheng, Bin1
2021-02-01
摘要This paper presents a novel decentralized multi-robot collision avoidance method with deep reinforcement learning, which is not only suitable for the large-scale grid map workspace multi-robot system, but also directly processes Lidar signals instead of communicating between the robots. According to the particularity of the workspace, we handcrafted a reward function, which considers both the collision avoidance among the robots and as little as possible change of direction of the robots during driving. Using Double Deep Q-Network (DDQN), the policy was trained in the simulation grid map workspace. By designing experiments, we demonstrated that the learned policy can guide the robot well to effectively travel from the initial position to the goal position in the grid map workspace and to avoid collisions with others while driving.
关键词robot learning deep reinforcement learning grid map workspace
DOI10.3390/s21030841
发表期刊SENSORS
卷号21期号:3页码:15
通讯作者Zheng, Bin(zhengbin@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000615487800001
语种英语