Comprehensive Application Analysis of Digital Twin System in Human Robot Collaboration

Qiyi Xie

doi:10.54097/s322yr33

Authors

Qiyi Xie

DOI:

https://doi.org/10.54097/s322yr33

Keywords:

Human-Robot Collaboration (HRC), Industry 5.0, Digital Twin Systems (DTs).

Abstract

Twin Systems (DTs) on Human-Robot Collaboration (HRC) within the context of Industry 5.0, drawing insights from 18 selected studies. It highlights three primary applications of DTs in HRC: optimizing production workflows through real-time simulation and analysis for efficiency enhancement, leveraging real-time data for dynamic task allocation to increase system flexibility, and improving worker safety by intelligently managing robot movements based on safety distance monitoring. In order to serve as a reference for upcoming researchers, this study provides an overview of recent research accomplishments in the primary applications of digital twin systems in HRC. This research explores Digital Twin Systems (DTs) in Human-Robot Collaboration (HRC) in Industry 5.0 in detail, emphasizing how they might improve productivity, adaptability, and safety. Furthermore, while examining the potential paths for digital twin evolution, the study's findings provide insightful information for upcoming investigations and applications pertaining to human-robot cooperation. This paper emphasizes how crucial DTs are to the development of intelligent, effective, and safe industrial processes.

Downloads

Download data is not yet available.

References

European Commission, “Industry 5.0”, [Online]. URL: https://research-and-innovation.ec.europa.eu/research-area/industrial research-and-innovation/industry-50_en. 2023.

Yao, B., Yang, B., Xu, W., Ji, Z., Zhou, Z., & Wang, L. Virtual data generation for human intention prediction based on digital modeling of human-robot collaboration. Robotics and Computer-Integrated Manufacturing, 87, 102714, 2024.

Zhu, Q., Huang, S., Wang, G., Moghaddam, S. K., Lu, Y., & Yan, Y. Dynamic reconfiguration optimization of Intelligent Manufacturing System with human-robot collaboration based on Digital Twin. Journal of Manufacturing Systems, 65, 330–338, 2022.

Choi, S. H., Park, K.-B., Roh, D. H., Lee, J. Y., Mohammed, M., Ghasemi, Y., & Jeong, H., 2022. An integrated mixed reality system for safety-aware human-robot collaboration using Deep Learning and Digital Twin Generation. Robotics and Computer-Integrated Manufacturing, 73, 102258, 2022.

Vásconez, J. P., & Auat Cheein, F. A. Workload and production assessment in the avocado harvesting process using human-robot collaborative strategies. Biosystems Engineering, 223, 56–77, 2022.

Nguyen, T. T., Parron, J., Obidat, O., Tuininga, A. R., & Wang, W. Ready or not? A robot-assisted Crop Harvest Solution in smart agriculture contexts. 2023 IEEE International Conference on Smart Computing (SMARTCOMP), 2023.

Xu, J., Li, W., Chen, P., Zhang, K., Huang, F., Jiang, Y., Zhang, Y., Liao, M., & Liu, M. Path planning method for Camellia oleifera forest trenching operation based on human-robot collaboration. Computers and Electronics in Agriculture, 215, 108446, 2023.

Dhanaraj, N., Ganesh, N., Gurav, R., Jeon, M., Manyar, O. M., Narayan, S., Park, J., Yu, Z., & Gupta, S. K. A human robot collaboration framework for assembly tasks in high mix manufacturing applications. Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, 2023.

Wang, K.-J., Lin, C. J., Tadesse, A. A., & Woldegiorgis, B. H. Modeling of human–robot collaboration for flexible assembly—a hidden semi-markov-based simulation approach. The International Journal of Advanced Manufacturing Technology, 126(11–12), 5371–5389, 2023.

Zhang, Z., Ji, Y., Tang, D., Chen, J., & Liu, C. Enabling Collaborative Assembly between humans and robots using a digital twin system. Robotics and Computer-Integrated Manufacturing, 86, 102691, 2024.

Li, C., Zheng, P., Li, S., Pang, Y., & Lee, C. K. M. AR-assisted Digital Twin-enabled robot collaborative manufacturing system with human-in-the-loop. Robotics and Computer-Integrated Manufacturing, 76, 102321, 2022.

Ye, Z., Jingyu, L., & Hongwei, Y. A digital twin-based human-robot collaborative system for the Assembly of complex-shaped architectures. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 095440542211109, 2022.

Zhang, R., Lv, J., Bao, J., & Zheng, Y. A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on Hierarchical Reinforcement Learning. Flexible Services and Manufacturing Journal, 35(4), 1116–1138, 2023.

Htet Oo, K., & Koomsap, P. Development of Collaborative Multi-Robotics Assembly system with Digital Twin Approach. Advances in Transdisciplinary Engineering.

Liu, S., Wang, X. V., & Wang, L. Digital Twin-enabled advance execution for Human-Robot Collaborative Assembly. CIRP Annals, 71(1), 25–28, 2022.

Liu, X., Zheng, L., Wang, Y., Yang, W., Jiang, Z., Wang, B., Tao, F., & Li, Y. Human-centric collaborative assembly system for large-scale space deployable mechanism driven by Digital Twins and wearable AR devices. Journal of Manufacturing Systems, 65, 720–742, 2022.

Yi, S., Liu, S., Xu, X., Wang, X. V., Yan, S., & Wang, L. A Vision-based human-robot collaborative system for Digital Twin. Procedia CIRP, 107, 552–557, 2022.

Park, J., Caroe Sorensen, L., Faarvang Mathiesen, S., & Schlette, C. A digital twin-based Workspace Monitoring System for Safe human-robot collaboration. 2022 10th International Conference on Control, Mechatronics and Automation (ICCMA), 2022.

Choi, S. H., Park, K.-B., Roh, D. H., Lee, J. Y., Mohammed, M., Ghasemi, Y., & Jeong, H. An integrated mixed reality system for safety-aware human-robot collaboration using Deep Learning and Digital Twin Generation. Robotics and Computer-Integrated Manufacturing, 73, 102258, 2022.

Wang, S., Zhang, J., Wang, P., Law, J., Calinescu, R., & Mihaylova, L. A deep learning-enhanced digital twin framework for improving safety and reliability in human–robot collaborative manufacturing. Robotics and Computer-Integrated Manufacturing, 85, 102608, 2024.

Zhang, R., Lv, J., Bao, J., & Zheng, Y. A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on Hierarchical Reinforcement Learning. Flexible Services and Manufacturing Journal, 35(4), 1116–1138,2023.

Berti, N., Finco, S., Guidolin, M., & Battini, D. Towards human digital twins to enhance workers’ safety and production system resilience. IFAC-PapersOnLine, 56(2), 11062–11067, 2023.

Liu, A., Zhang, Y., & Yao, Y. Digital Twins for hand gesture-guided human-robot collaboration systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2024.

Baratta, A., Longo, F., Mirabelli, G., Padovano, A., & Solina, V. A digital twin-based approach for emotion recognition in human-robot collaboration. Towards a Smart, Resilient and Sustainable Industry, 155–166, 2023.

Zhong, R., Hu, B., Feng, Y., Zheng, H., Hong, Z., Lou, S., & Tan, J. Construction of human digital twin model based on multimodal data and its application in Locomotion Mode Identification. Chinese Journal of Mechanical Engineering, 36(1), 2023.

Si, W., Zhong, T., Wang, N., & Yang, C. A multimodal teleoperation interface for human-robot collaboration. 2023 IEEE International Conference on Mechatronics (ICM), 2023.

Ma, X., Qi, Q., & Tao, F. A digital twin–based environment-adaptive assignment method for Human–Robot Collaboration. Journal of Manufacturing Science and Engineering, 146(3), 2023.

Lee, H., Kim, S. D., & Amin, M. A. Control Framework for collaborative robot using imitation learning-based teleoperation from human digital twin to Robot Digital Twin. Mechatronics, 85, 102833, 2022.