A reinforcement learning method for human-robot collaboration in assembly tasks

The assembly process of high precision products involves a variety of delicate operations that are timeconsuming and energy-intensive. Neither the human operators nor the robots can complete the tasks independently and efficiently. The human-robot collaboration to be applied in complex assembly operation would help reduce human workload and improve efficiency. However, human behavior can be unpredictable in assembly activities so that it is difficult for the robots to understand intentions of the human operations. Thus, the collaboration of humans and robots is challenging in industrial applications. In this regard, a human-robot collaborative reinforcement learning algorithm is proposed to optimize the task sequence allocation scheme in assembly processes. Finally, the effectiveness of the method is verified through experimental analysis of the virtual assembly of an alternator. The result shows that the proposed method had great potential in dynamic division of human-robot collaborative tasks.

Automated summary

The assembly process of high precision products requires human-robot collaboration to optimize efficiency, but the unpredictability of human behavior poses a challenge. A human-robot collaborative reinforcement learning algorithm has been proposed and validated through experimental analysis to optimize task allocation in assembly processes.