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

被引:0
|
作者
Bao J. [1 ,2 ]
Zhang R. [1 ]
Li J. [1 ]
Lu Y. [3 ]
Peng T. [4 ]
机构
[1] College of Mechanical Engineering, Donghua University, Shanghai
[2] State Key Laboratory for Modification of Chemical Fibers and Ploymer Materials, Shanghai
[3] Department of Mechanical Engineering, The University of Auckland, Auckland
[4] School of Mechanical Engineering, Zhejiang University, Hangzhou
关键词
digital twin; environment understanding; human-robot collaboration; reinforcement learning; transfer learning;
D O I
10.3901/JME.2022.18.103
中图分类号
学科分类号
摘要
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. © 2022 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.
引用
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页码:103 / 115
页数:12
相关论文
共 36 条
  • [1] FACCIO M, MINTO R, ROSATI G,, Et al., The influence of the product characteristics on human-robot collaboration : A model for the performance of collaborative robotic assembly[J], The International Journal of Advanced Manufacturing Technology, 106, 5, pp. 2317-2331, (2020)
  • [2] WANG Fei, QI Huan, ZHOU Xingqun, Et al., Demonstration programming and optimization method of cooperative robot based on multi-source information fusion[J], Robot, 40, 4, pp. 551-559, (2018)
  • [3] KONG Fansen, GAO Tianyu, LI Huimin, Et al., Research on human-robot joint task assignment considering task complexity[J], Journal of Mechanical Engineering, 57, 7, pp. 204-214, (2021)
  • [4] RAHMAN S, WANG Y., Mutual trust-based subtask allocation for human-robot collaboration in flexible lightweight assembly in manufacturing[J], Mechatronics, 54, pp. 94-109, (2018)
  • [5] NIKOLAKIS N, MARATOS V, MAKRIS S., A cyber physical system (CPS) approach for safe human-robot collaboration in a shared workplace[J], Robotics and Computer-Integrated Manufacturing, 56, pp. 233-243, (2019)
  • [6] WANG Baicun, ZANG Jiyuan, QU Xianming, Et al., Research on new-generation intelligent manufacturing based on human-cyber-physical systems[J], Strategic Study of CAE, 20, 4, pp. 29-34, (2018)
  • [7] WANG Baicun, YI Bin, ZHOU Zhenyu, Et al., Evolution and State-of-the -art of intelligent manufacturing friom HCPS perspective[J], Computer Integrated Manufacturing Systems, 27, 10, pp. 2749-2761, (2021)
  • [8] ZHOU J, ZHOU Y, WANG B, Et al., Human-cyber-physical systems (HCPSs) in the context of new-generation intelligent manufacturing[J], Engineering, 5, 4, pp. 624-636, (2019)
  • [9] ZHOU J,, LI P,, ZHOU Y, Et al., Toward new-generation intelligent manufacturing[J], Engineering, 4, 1, pp. 11-20, (2018)
  • [10] BI Z M,, LUO C, MIAO Z, Et al., Safety assurance mechanisms of collaborative robotic systems in manufacturing[J], Robotics and Computer-Integrated Manufacturing, 67, (2021)