1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China;
2. Huazhong University of Science and Technology, Wuhan 430074, China
The digital twin model of industrial robot can simulate the behavior and performance of industrial robot in the real world, but its simulation accuracy will be reduced due to the influence of service conditions such as scene update and equipment wear. In this paper, an update method of digital twin model of industrial robots based on deep reinforcement learning is proposed. In this method, the simulation tool Coppeliasim is used to establish the digital twin model of industrial
robots. At the same time, the key parameters of the digital twin model such as PID parameters and joint damping are optimized based on the depth deterministic strategy gradient (DDPG) algorithm, so as to realize the parameter update of the model and improve the model accuracy. Finally, the simulation experiment of ABB–IRB2400 industrial robot is carried out to verify the effectiveness of the proposed method.
段现银，秦志强，唐小卫，向峰. 基于深度强化学习的工业机器人数字孪生模型更新方法[J]. 航空制造技术, 2024, 67(11): 48-55.
DUAN Xianyin, QIN Zhiqiang, TANG Xiaowei, XIANG Feng. Update Method of Digital Twin Model of Industrial Robot Based on Deep Reinforcement Learning[J]. Aeronautical Manufacturing Technology, 2024, 67(11): 48-55.
2024, 
