Update Method of Digital Twin Model of Industrial Robot Based on Deep Reinforcement Learning

DUAN Xianyin; QIN Zhiqiang; TANG Xiaowei; XIANG Feng

doi:10.16080/j.issn1671-833x.2024.11.048

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Update Method of Digital Twin Model of Industrial Robot Based on Deep Reinforcement Learning

更新时间：2025-10-30

- Update Method of Digital Twin Model of Industrial Robot Based on Deep Reinforcement Learning
- Aeronautical Manufacturing Technology Vol. 67, Issue 11, Pages: 48-55(2024)
- 作者机构：
  
  1. 武汉科技大学冶金装备及其控制教育部重点实验室,武汉,430081
  2. 华中科技大学,武汉,430074
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2024.11.048
  CLC：
- Published：2024
- 稿件说明：
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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.
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. DOI： 10.16080/j.issn1671-833x.2024.11.048.

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. DOI： 10.16080/j.issn1671-833x.2024.11.048.

摘要

工业机器人的数字孪生模型能够模拟真实世界中工业机器人的行为和性能，但其仿真精度会受场景更新和设备磨损等使役工况的影响而下降。对此，本文提出了一种基于深度强化学习的工业机器人数字孪生模型更新方法。该方法应用仿真工具Coppeliasim建立了工业机器人数字孪生模型，同时基于深度确定性策略梯度（DDPG）算法对数字孪生模型的PID参数、关节阻尼等关键参数进行优化，实现模型的参数更新，提高模型精度。最后，通过ABB–IRB2400工业机器人仿真同步试验，验证了所提方法的有效性。

Abstract

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.

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