Task reallocation of human-robot collaborative production workshop based on a dynamic human fatigue model

As collaborative robots become more popular in industry, they are used to collaborate with human workers and free human workers from repeated and heavy work and reduce the risk of physical injuries. However, human workers need to finish tasks that require high cognitive abilities, which increases the risk of mental fatigue. Fatigue can negatively impact workers and even threaten the safety of workers. Currently, the dynamics of human fatigue model are rarely considered. Hence, it is crucial to develop a dynamic allocation method for human-robot collaborative tasks that considers dynamic human fatigue to ensure efficient and safe human-robot collaboration. This paper proposes a task reallocation method for human-robot collaborative production workshop based on a dynamic human fatigue model. A fatigue feature extraction method from multimodal data and a fatigue evaluation network are proposed to evaluate the current fatigue status of human workers. Based on the evaluation results, the human fatigue model is dynamically updated. If the worker's fatigue state changes, a task reallocation scheme is generated by an improved genetic algorithm based on reinforcement learning and the dynamic human fatigue model. The experimental results show that the proposed model and algorithm are effective and efficient, and can provide innovative insights for realizing a human-centered manufacturing workshop.