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Open Access

Research on Digital Twin System Platform Framework and Key Technologies of Unmanned Ground Equipment

Kunyu Wang1, Lin Zhang1,2( ), Cheng Xu3, Han Lu1, Zhen Chen1, Hongbo Cheng1, Rui Guo3
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
State Key Laboratory of Intelligent Manufacturing System Technology, Beijing 100854, China
Beijing Institute of Electronic System Engineering, Beijing 100854, China
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Abstract

As an emerging technology, digital twin is expected to bring novel application modes to the whole life cycle process of unmanned ground equipment, including research and development, design, control optimization, operation and maintenance, etc. The highly dynamic, complex, and uncertain characteristics of unmanned ground equipment and the battlefield environment also pose new challenges for digital twin technology. Starting from the new challenges faced by the digital twin of unmanned ground equipment, this paper designs a service-oriented cloud-edge-end collaborative platform architecture of the digital twin system of unmanned ground equipment, and further analyzes several key technologies supporting the implementation of the platform architecture.

Keywords

artificial intelligencedigital twinunmanned ground equipmentnative cloud platformModeling and Simulation (M&S)

References

[1]

J. Zhang, P. D. Xu, and F. Y. Wang, Parallel systems and digital twins: A data-driven mathematical representation and computational framework, (in Chinese), Acta Automatica Sinica, vol. 46, no. 7, pp. 1346–1356, 2020.
Google Scholar
[2]

Y. P. Wang, S. B. Zhu, and C. Q. Li, Operational test and evaluation framework based on parallel system and machine learning, (in Chinese), Journal of Command and Control, vol. 8, no. 3, pp. 311–317, 2022.
Google Scholar
[3]

W. M. Lang, W. G. Ma, Z. P. Zhao, Z. Y. Wang, and Y. G. Li, Research on digital twin equipment of US army, (in Chinese), Telecommunication Express, vol. 616, no. 10, pp. 1–6, 2022.
Google Scholar
[4]
S. Li, Q. Yang, J. Xing, and S. Yuan, Preliminary study on the application of digital twin in military engineering and equipment, in Proc. Chinese Automation Congress (CAC), Shanghai, China, 2020, pp. 7249–7255.
Crossref
[5]

Y. C. Wang, N. Zhang, H. Li, and J. Cao, Research on digital twin framework of military large-scale UAV based on cloud computing, J. Phys.: Conf. Ser., vol. 1738, no. 1, p. 012052, 2021.
Crossref Google Scholar
[6]

M. Song, Q. Shi, Q. Hu, Z. You, and L. Chen, On the architecture and key technology for digital twin oriented to equipment battle damage test assessment, Electronics, vol. 12, no. 1, p. 128, 2023.
Crossref Google Scholar
[7]

P. Wang, M. Yang, J. Zhu, Y. Peng, and G. Li, Digital twin-enabled online battlefield learning with random finite sets, Comput. Intell. Neurosci., vol. 2021, p. 5582241, 2021.
Crossref Google Scholar
[8]

N. Zhang, J. Guo, S. P. Yin, and Z. Y. Gao, Development of digital twin technology and its application prospect in unmanned undersea system, (in Chinese), Journal of Unmanned Undersea Systems, vol. 30, no. 2, pp. 137–146, 2022.
Google Scholar
[9]

X. Li, Technical difficulties and development prospects of the digital twin naval gun, (in Chinese), Journal of Gun Lanuch & Control, vol. 44, no. 1, pp. 30–35, 2023.
Google Scholar
[10]

P. Wang, M. Yang, J. C. Zhu, R. S. Ju, and G. Li, Dynamic data driven modeling and simulation method for digital twin, (in Chinese), Systems and Engineering and Electronics, vol. 42, no. 12, pp. 2779–2786, 2020.
Google Scholar
[11]

L. Zhang and H. Lu, Discussing digital twin from of modeling and simulation, (in Chinese), Journal of System Simulation, vol. 33, no. 5, pp. 995–1007, 2021.
Google Scholar
[12]
Z. Q. Wei, G. Q. Shi, T. Y. Lin, and L. Q. Guo, A design of cloud simulation system based on container, (in Chinese), presented at 33th China Simulation Conference, Beijing, China, 2021.
[13]

B. H. Li, X. D. Chai, Y. Liu, L. Chen, and D. Y. Wei, Development strategy of smart Internet of Things system, (in Chinese), Strategic Study of CAE, vol. 24, no. 4, pp. 1–11, 2022.
Crossref Google Scholar
[14]

G. G. Seo, Y. Kim, and S. Saderla, Kalman-filter based online system identification of fixed-wing aircraft in upset condition, Aerospace Science and Technology, vol. 89, pp. 307–317, 2019.
Crossref Google Scholar
[15]

T. I. Zohdi, A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety, Comput. Meth. Appl. Mech. Eng., vol. 373, p. 113446, 2021.
Crossref Google Scholar
[16]
J. W. Song, Y. I. Park, J. J. Hong, S. G. Kim, and S. J. Kang, Attention-based bidirectional LSTM-CNN model for remaining useful life estimation, in Proc. IEEE Int. Symp. on Circuits and Systems (ISCAS), Daegu, Republic of Korea, 2021, pp. 1–5.
Crossref
[17]

Z. Xue, Y. Zhang, C. Cheng, and G. Ma, Remaining useful life prediction of lithium-ion batteries with adaptive unscented Kalman filter and optimized support vector regression, Neurocomputing, vol. 376, pp. 95–102, 2020.
Crossref Google Scholar
[18]

H. Chen, L. Li, C. Shang, and B. Huang, Fault detection for nonlinear dynamic systems with consideration of modeling errors: A data-driven approach, IEEE Trans. Cybern., vol. 53, no. 7, pp. 4259–4269, 2023.
Crossref Google Scholar
[19]
M. Lutter, C. Ritter, and J. Peters, Deep Lagrangian networks: Using physics as model prior for deep learning, arXiv preprint arXiv: 1907.04490, 2019.
[20]

W. Luo, T. Hu, Y. Ye, C. Zhang, and Y. Wei, A hybrid predictive maintenance approach for CNC machine tool driven by digital twin, Robot. Comput. Integr. Manuf., vol. 65, p. 101974, 2020.
Crossref Google Scholar
[21]
A. Karpatne, W. Watkins, J. Read, and V. Kumar, Physics-guided neural networks (PGNN): An application in lake temperature modeling, arXiv preprint arXiv: 1710.11431, 2017.
[22]

S. Cuomo, V. S. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli, Scientific machine learning through physics–informed neural networks: Where we are and what’s next, Journal of Scientific Computing, vol. 92, no. 3, p. 88, 2022.
Crossref Google Scholar
[23]
R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud, Neural ordinary differential equations, in Proc. NIPS'18: 32nd Int. Conf. Neural Information Processing Systems, Montréal, Canada, 2018, pp. 6572–6583.
[24]

A. Thelen, X. Zhang, O. Fink, Y. Lu, S. Ghosh, B. D. Youn, M. D. Todd, S. Mahadevan, C. Hu, and Z. Hu, A comprehensive review of digital twin—Part 2: Roles of uncertainty quantification and optimization, a battery digital twin, and perspectives, Struct. Multidiscip. Optim., vol. 66, no. 1, p. 1, 2023.
Crossref Google Scholar
[25]

L. Zhang, L. Zhou, and B. K. P. Horn, Building a right digital twin with model engineering, J. Manuf. Syst., vol. 59, pp. 151–164, 2021.
Crossref Google Scholar
[26]

L. Wright and S. Davidson, How to tell the difference between a model and a digital twin, Adv. Model. Simul. Eng. Sci., vol. 7, no. 1, p. 13, 2020.
Crossref Google Scholar
[27]
R. Ward, R. Choudhary, A. Gregory, M. Jans-Singh, and M. Girolami, Continuous calibration of a digital twin: Comparison of particle filter and Bayesian calibration approaches, arXiv preprint arXiv: 2011.09810, 2020.
Crossref
[28]

Y. He and B. Sick, CLeaR: An adaptive continual learning framework for regression tasks, AI Perspect., vol. 3, no. 1, p. 2, 2021.
Crossref Google Scholar
[29]

L. Zhang, X. S. Zhang, X. Song, B. H. Li, F. Tao, and Y. Liu, Model engineering for complex system simulation, (in Chinese), Journal of System Simulation, vol. 25, no. 11, pp. 2729–2736, 2013.
Google Scholar
[30]
L. Zhang, F. Ye, Y. Laili, K. Y. Xie, P. F. Gu, X. H. Wang, C. Zhao, X. S. Zhang, and M. J. Chen, X language: An integrated intelligent modeling and simulation language for complex products, in Proc. 2021 Annual Modeling and Simulation Conference (ANNSIM), Fairfax, VA, USA, 2021, pp. 1–11.
Crossref
Complex System Modeling and Simulation
Volume 4 Issue 2,
June 2024
Pages 109-123
DOI: 10.23919/CSMS.2024.0009
Cite this article:
Wang K, Zhang L, Xu C, et al. Research on Digital Twin System Platform Framework and Key Technologies of Unmanned Ground Equipment. Complex System Modeling and Simulation, 2024, 4(2): 109-123. https://doi.org/10.23919/CSMS.2024.0009

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Received: 25 November 2023
Revised: 10 April 2024
Accepted: 10 May 2024
Published: 30 June 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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