MADDPG based Assembly Approach of Cooperative Robotic Arms

LI Yanjiang; WANG Chensheng; YANG Guang; JING Xueliang; LI Yangguang; QIAN Zhixuan

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MADDPG based Assembly Approach of Cooperative Robotic Arms

LI Yanjiang 1,WANG Chensheng 1 *,YANG Guang 2,JING Xueliang 2,LI Yangguang 2,QIAN Zhixuan 2

1.School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876;School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876;School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876;School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876;School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876;School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876

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Funding: Jiangsu Province Key Research Project on Industry Prospect and Common Key Technologies （No.BE2016032）, Wuxi Science and Technology Development Project on Industry Prospect and Common Technology （No.CGE02G1609）

Opened online:22 June 2020

Accepted by: none

Citation: LI Yanjiang,WANG Chensheng,YANG Guang.MADDPG based Assembly Approach of Cooperative Robotic Arms[OL]. [22 June 2020] http://en.paper.edu.cn/en_releasepaper/content/4752399

Compared with the single robitic arm, a two-arm cooperative robot has a more complex problem of motion interference. In cooperative assembly problems, traditional control methods need much calculation to establish a mathematical model for motion planning, which can not meet the needs of modern industry. Multi-Agent Reinforcement Learning(MARL) provides a new way to solve the assembly problem. This paper proposes a assembly method of collaborative robotic arm based on MADDPG. A multi-agent system simulation environment is founded based on MuJoCo physical engine and then assembly tasks in the environment are solved with MADDPG model. Finally, the approach is proved to be effective by experiment.

Keywords:multi-agent reinforcement learning; robotic arm control; multi-agent system

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