• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2022, Vol. 58 ›› Issue (18): 103-115.doi: 10.3901/JME.2022.18.103

• 技术开发 • 上一篇    下一篇

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面向人-机-环境共融的数字孪生协同技术

鲍劲松1,2, 张荣1, 李婕1, 陆玉前3, 彭涛4   

  1. 1. 东华大学机械工程学院 上海 201620;
    2. 东华大学纤维材料改性国家重点实验室 上海 201620;
    3. 奥克兰大学机械工程系 奥克兰 1142 新西兰;
    4. 浙江大学机械工程学院 杭州 310027
  • 收稿日期:2021-11-01 修回日期:2022-03-20 出版日期:2022-09-20 发布日期:2022-12-08
  • 通讯作者: 鲍劲松(通信作者),男,1973年出生,教授,博士研究生导师。主要研究方向为智能制造、虚拟现实/人机交互技术等。E-mail:bao@dhu.edu.cn
  • 作者简介:张荣,男,1994年出生,博士研究生。主要研究方向为协作机器人、人机共融以及智能化装配等;E-mail:1199074@mail.dhu.edu.cn;李婕,女,1989年出生,博士。主要研究方向为绿色制造以及智能拆解等;E-mail:jie.li@dhu.edu.cn;陆玉前,男,1990年出生,博士研究生导师。主要研究方向为智能制造、人机共融、工业人工智能;E-mail:yuqian.lu@auckland.ac.nz;彭涛,男,1984年出生,博士,副教授,硕士研究生导师。主要研究方向为可持续智能制造、工业大数据、感知智能与认知智能;E-mail:tao_peng@zju.edu.cn
  • 基金资助:
    国家重点研发计划资助项目(2019YFB1706300)。

Digital-twin Collaborative Technology for Human-robot-environment Integration

BAO Jinsong1,2, ZHANG Rong1, LI Jie1, LU Yuqian3, PENG Tao4   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620;
    2. State Key Laboratory for Modification of Chemical Fibers and Ploymer Materials, Shanghai 201620;
    3. Department of Mechanical Engineering, The University of Auckland, Auckland 1142, New Zealand;
    4. School of Mechanical Engineering, Zhejiang University, Hangzhou 310027
  • Received:2021-11-01 Revised:2022-03-20 Online:2022-09-20 Published:2022-12-08

摘要: 数字孪生正在制造系统中发挥重要作用,然而在面向人机协助完成的复杂制造场景中,人-机-环境及其构成的数字孪生系统呈现出任务异构复杂、环境动态多变及其交互实时等特点。目前欠缺人-机-环境共融的数字孪生协同过程中智能方法相关研究,尤其是数字孪生模型在协同中的迁移和强化,以满足制造系统的鲁棒性和自适应能力。提出面向人-机-环境共融的数字孪生协同技术,从环境和任务两个核心来展开数字孪生协同的人机共融科学问题。首先给出协作装配环境的数字孪生体系,以虚拟装配的形式为人-机-任务交互提供理解;建立相应的空间模型与协同模型,为共融的孪生协同提供理论支持;最后,以最典型的人机共融制造场景(装配任务)为案例,在决策层基于迁移学习算法为机器人提供装配操作指引,同时通过强化学习算法优化机器人的具体执行动作。在不同型号产品的人机协同装配任务中,均可以生成相应的人机协作装配规划方案,证明了所提方法的可行性。

关键词: 人机协作, 环境理解, 数字孪生, 迁移学习, 强化学习

Abstract: Digital twin is playing an important role in manufacturing system. However, in the complex manufacturing scene for human-robot collaboration, human-robot-environment and its digital twin system show the characteristics of heterogeneous and complex tasks, dynamic environment and real-time interaction. At present, the research on intelligent methods in the digital twin collaboration process of human-robot-environment integration is poor, especially the transfer and reinforcement of digital twin model in collaboration, so as to meet the robustness and adaptive ability of manufacturing system. The paper puts forward the digital twin collaboration technology for human-robot-environment integration, and launches the scientific problem of human -robot integration in digital twin collaboration from the two cores of environment and task. Firstly, the digital twin model of collaborative assembly environment is given to provide understanding for human-robot-task interaction in the form of virtual assembly; Secondly, the corresponding spatial model and collaboration model are established to provide theoretical support for the twin collaboration of integration; Finally, taking the most typical human-robot integrated manufacturing scenario (assembly task) as an example, the transfer learning algorithm is used to provide assembly operation guidance for the robot at the decision-making level, and the reinforcement learning algorithm is used to optimize the specific execution actions of the robot. In different types of products, the corresponding human-robot collaborative assembly planning schemes can be generated, which proves the feasibility of the proposed method.

Key words: human-robot collaboration, environment understanding, digital twin, transfer learning, reinforcement learning

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