Towards seamless and safe human-robot collaboration in Industry 5.0: advances in human behaviour prediction.
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| Title: | Towards seamless and safe human-robot collaboration in Industry 5.0: advances in human behaviour prediction. |
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| Authors: | Du, Yuyang1,2 (AUTHOR), Zhang, Bi2 (AUTHOR), Zheng, Chen1 (AUTHOR) chen.zheng@nwpu.edu.cn, Tang, Yunlong3,4 (AUTHOR) yunlong.tang1@monash.edu |
| Source: | International Journal of Production Research. Jun2026, Vol. 64 Issue 12, p4770-4807. 38p. |
| Subjects: | Human-robot interaction, Human behavior models, Industry 4.0, Accident prevention, Machine learning, Factory safety, Flexible manufacturing systems, Prediction theory |
| Abstract: | In the era of Industry 5.0, human-centered manufacturing systems are gaining prominence, emphasising the integration of the human worker's ingenuity, flexibility, and dexterity with the robot's strength, precision, power and speed, which indicates the transition to seamless Human-Robot Collaboration (HRC). However, the close proximity between human workers and robots also raises safety issues, which necessitates the human behaviour prediction to avoid potential collisions, risks and injuries. This review comprehensively explores the advancements in human behaviour prediction methods in HRC, focusing on mechanism-based, data-based, and mechanism and data-fusion-based human behaviour prediction methods. Then, the major characteristics, limitations, and applicability of the approaches are analysed. Finally, the existing challenges and future directions for human behaviour prediction are discussed, and a unified human behaviour prediction framework is proposed to improve the safety, efficiency, and generalisation in human-centric manufacturing systems. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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