Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 1: 11 - 11

DOI: https://doi.org/10.1186/s10033-022-00680-w

Research Highlight

Human Digital Twin (HDT) Driven Human-Cyber-Physical Systems: Key Technologies and Applications

  • Baicun Wang ,
  • Huiying Zhou ,
  • Geng Yang ,
  • Xingyu Li ,
  • Huayong Yang
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  • 1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;
    2. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

收稿日期: 2021-10-22

  网络出版日期: 2022-06-30

基金资助

Supported by National Natural Science Foundation of China (Grant Nos. 51975513, 51890884), Zhejiang Provincial Natural Science Foundation of China (Grant No. LR20E050003), and Major Research Plan of Ningbo Innovation 2025 (Grant No. 2020Z022)

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Human Digital Twin (HDT) Driven Human-Cyber-Physical Systems: Key Technologies and Applications

  • Baicun Wang ,
  • Huiying Zhou ,
  • Geng Yang ,
  • Xingyu Li ,
  • Huayong Yang
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  • 1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;
    2. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

Received date: 2021-10-22

  Online published: 2022-06-30

Supported by

Supported by National Natural Science Foundation of China (Grant Nos. 51975513, 51890884), Zhejiang Provincial Natural Science Foundation of China (Grant No. LR20E050003), and Major Research Plan of Ningbo Innovation 2025 (Grant No. 2020Z022)

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本文引用格式

Baicun Wang , Huiying Zhou , Geng Yang , Xingyu Li , Huayong Yang . Human Digital Twin (HDT) Driven Human-Cyber-Physical Systems: Key Technologies and Applications[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(1) : 11 -11 . DOI: 10.1186/s10033-022-00680-w

参考文献

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[2] M Zhao, Z B Ning, B C Wang, et al. Understanding the evolution and applications of intelligent systems via a Tri-X intelligence (TI) model. Processes, 2021, 9(6):1080.
[3] M Breque, L D Nul, A Petridis. Industry 5.0:Towards a sustainable, human-centric and resilient European industry. European Commission, Brussels, Belgium, 2021.
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[12] B C Wang, X Y Li, T Freiheit, et al. Learning and intelligence in Human-Cyber-Physical Systems:Framework and perspective. 2020 Second International Conference on Transdisciplinary AI (TransAI), Irvine, CA, USA, Sept. 21-23, 2020:142-145.
[13] Y Xu, C Ding, X K Shu, et al. Shared control of a robotic arm using non-invasive brain-computer interface and computer vision guidance. Robotics and Autonomous Systems, 2019, 115:121-129.
[14] Z X Zhu, C Liu, X Xu. Visualisation of the digital twin data in manufacturing by using augmented reality. Procedia CIRP, 2019, 81:898-903.
[15] B Siciliano, O Khatib. Springer handbook of robotics. Springer, Berlin, Heidelberg, Germany, 2008.
[16] A Ajoudani, C Fang, N Tsagarakis, et al. Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation. The International Journal of Robotics Research, 2018, 37(1):155-167.
[17] P M Kebria, H Abdi, M M Dalvand, et al. Control methods for internet-based teleoperation systems:A review. IEEE Transactions on Human-Machine Systems, 2019, 49(1):32-46.
[18] G Yang, H H Lv, Z Y Zhang, et al. Keep healthcare workers safe:application of teleoperated robot in isolation ward for COVID-19 prevention and control. Chinese Journal of Mechanical Engineering, 2020, 33:47.
[19] Y Su, T Wang, K Zhang, et al. Adaptive nonlinear control algorithm for a self-balancing robot. IEEE Access, 2020, 8:3751-3760.
[20] TEODOR EVO. https://www.telerob.com/de/produkte/teodor-evo. 2021-10-22.
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