LLM-based multi-agent task planning for human-robot collaborative assembly balancing operator experience and efficiency

Abstract

In human-robot collaborative assembly (HRCA), systematic task planning is required to enhance the coordination between human and robot, prevent execution conflicts, and improve assembly efficiency. However, traditional HRCA task planning methods are often tailored to specific tasks, lacking generality and requiring significant manual involvement. Meanwhile, overemphasis on efficiency neglects the work experience of operators. This paper proposes MATP, a multi-agent task planning method for HRCA based on large language models (LLMs), aimed at enhancing human-robot collaboration (HRC), avoiding execution conflicts, and balancing operator experience with production efficiency. The method creates multiple agents, each of which explicitly defines its distinct role and responsibilities such that they can collaboratively plan HRCA tasks. A standardized and automated planning process is developed where the input is the assembly task and the output is the generated optimal human-robot task allocation sequence. Specifically, MATP firstly decomposes assembly tasks into action-level subtasks. Then, it evaluates the states of both the operator and the robot from multiple perspectives including fatigue, postural comfort and human-robot trust. Task allocation is finally achieved through deep collaboration between the LLM and the genetic algorithm (GA). Validation in the electronic product assembly scenario demonstrate that MATP outperforms single-agent and traditional method in HRCA task planning. In addition, it effectively balances operator experience and assembly efficiency, significantly enhancing planning efficiency and dynamic adaptability.