The decision-making framework for assembly tasks planning in human–robot collaborated manufacturing system

ABSTRACT

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.

KEYWORDS:

• Human–robot collaboration
• task planning
• assembly allocation and scheduling
• Multi-objective optimization

Acknowledgments

The authors would like to thank the National Key R&D Program of China (Project No. 2019YFB1704001) and the National Natural Science Foundation of China (Project No. 52175451).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [2019YFB1704001]; National Natural Science Foundation of China [52175451].