A dynamic task allocation framework for human-robot collaborative assembly based on digital twin and IGA-TS
Human-robot collaborative assembly is recognized as an essential component of intelligent manufacturing systems, combining human flexibility with machine efficiency, thereby enhancing the effectiveness and adaptability of assembly tasks. However, challenge in adaptability, decision-making, and respo...
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| Vydáno v: | Journal of manufacturing systems Ročník 80; s. 206 - 223 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.06.2025
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| ISSN: | 0278-6125 |
| On-line přístup: | Získat plný text |
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| Abstract | Human-robot collaborative assembly is recognized as an essential component of intelligent manufacturing systems, combining human flexibility with machine efficiency, thereby enhancing the effectiveness and adaptability of assembly tasks. However, challenge in adaptability, decision-making, and responsiveness to changing scenarios persist. To address these, this paper propose a digital twin-driven decision-making approach for task allocation, using an Improved Genetic Algorithm with Tabu Search (IGA-TS). First, an assembly task evaluation model and digital twin framework are developed to support dynamic decision-making. Subsequently, the IGA-TS algorithm integrates a custom encoding scheme, fitness function, tabu list, and neighborhood search to avoid local optima, enhancing global optimization and convergence speed. Lastly, a digital twin-assisted system, combining human body modeling and motion recognition, enables real-time optimization feedback, forming a closed-loop for collaboration. Experimental results show that IGA-TS outperforms traditional genetic algorithms and heuristic methods in multi-objective optimization, reducing assembly time, task complexity, and human workload. In addition, the designed digital twin system demonstrates strong adaptability and robustness in responding to dynamic changes during the assembly process, providing a practical and feasible solution for manufacturing workshop assembly. It significantly enhances production efficiency and product quality, offering substantial industrial application value.
•Propose a digital twin-driven decision-making framework for task allocation.•Constructs an improved genetic algorithm to achieve optimal solutions.•Combines Tabu Search to form the IGA-TS algorithm for multi-objective optimization.•Develops a digital twin-assisted system enabling dynamic optimization feedback. |
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| AbstractList | Human-robot collaborative assembly is recognized as an essential component of intelligent manufacturing systems, combining human flexibility with machine efficiency, thereby enhancing the effectiveness and adaptability of assembly tasks. However, challenge in adaptability, decision-making, and responsiveness to changing scenarios persist. To address these, this paper propose a digital twin-driven decision-making approach for task allocation, using an Improved Genetic Algorithm with Tabu Search (IGA-TS). First, an assembly task evaluation model and digital twin framework are developed to support dynamic decision-making. Subsequently, the IGA-TS algorithm integrates a custom encoding scheme, fitness function, tabu list, and neighborhood search to avoid local optima, enhancing global optimization and convergence speed. Lastly, a digital twin-assisted system, combining human body modeling and motion recognition, enables real-time optimization feedback, forming a closed-loop for collaboration. Experimental results show that IGA-TS outperforms traditional genetic algorithms and heuristic methods in multi-objective optimization, reducing assembly time, task complexity, and human workload. In addition, the designed digital twin system demonstrates strong adaptability and robustness in responding to dynamic changes during the assembly process, providing a practical and feasible solution for manufacturing workshop assembly. It significantly enhances production efficiency and product quality, offering substantial industrial application value.
•Propose a digital twin-driven decision-making framework for task allocation.•Constructs an improved genetic algorithm to achieve optimal solutions.•Combines Tabu Search to form the IGA-TS algorithm for multi-objective optimization.•Develops a digital twin-assisted system enabling dynamic optimization feedback. |
| Author | Lu, Hongjiang Cao, Xinjie Yu, Chunyang Liu, Lilan Gao, Zenggui Tang, Jingwei Yao, Yuyan |
| Author_xml | – sequence: 1 givenname: Zenggui orcidid: 0000-0001-6216-9387 surname: Gao fullname: Gao, Zenggui organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 2 givenname: Jingwei surname: Tang fullname: Tang, Jingwei organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 3 givenname: Hongjiang surname: Lu fullname: Lu, Hongjiang organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 4 givenname: Yuyan surname: Yao fullname: Yao, Yuyan organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 5 givenname: Xinjie surname: Cao fullname: Cao, Xinjie organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 6 givenname: Chunyang surname: Yu fullname: Yu, Chunyang email: yuchunyang@caa.edu.cn organization: Design-AI Lab, China Academy of Art, Hangzhou 310024, China – sequence: 7 givenname: Lilan orcidid: 0009-0006-4186-1538 surname: Liu fullname: Liu, Lilan email: lancy_shu@163.com organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China |
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| Keywords | Multi-objective optimization Human-robot collaborative assembly Digital twin Improved genetic algorithm Dynamic task allocation |
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