期刊
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
卷 36, 期 2, 页码 289-307出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0951192X.2022.2081359
关键词
Human-robot collaboration; task planning; assembly allocation and scheduling; Multi-objective optimization
This paper proposes a decision-making framework for the task planning problem in human-robot collaborative assembly systems. It considers task decomposition, resource evaluation, operation scheduling, and collaboration between humans and robots. A joint optimization model is developed to minimize competition time and production costs while improving the automation degree of the hybrid system. Computational results based on industrial cases demonstrate the performance and feasibility of the proposed methodology.
A human-robot collaborative assembly system is a new production paradigm, which has been successfully applied in practical production to effectively combine human flexibility and robot productivity. This paper proposes the task planning problem for human and robot collaboration and considers the whole process from assembly task decomposition, assembly to human and robot, and operations scheduling in hybrid assembly systems. The decision-making framework for the task planning problem is first introduced from five steps: data input, task decomposition, resource evaluation, operation allocation and scheduling, and collaborative assembly implementation. The task decomposition process is performed based on the hierarchical task analysis approach. The assembly operation allocation and scheduling are then considered in an integrated way, and the joint optimization model is developed considering the assembly operation sequence and the collaboration of humans and robots. Triple objectives are considered not only to minimize the competition time and total production costs but also to improve the automation degree of the hybrid system. The improved heuristic algorithm is developed to address the joint optimization problem. Finally, the application of this decision-making framework is described and verified based on two industrial cases. Computational results are presented to show the performance and feasibility of the proposed methodology.
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