Dynamic task reallocation in human-robot collaborative workshop based on online biotic fatigue detection

Abstract

Collaborative robots have been introduced into manufacturing workshops to collaborate with human and improve production flexibility and ergonomics. In human-robot collaboration (HRC) workshops, detecting the fatigue state of workers and reassigning tasks online quickly is the key to effectively avoid product quality defects, safety incidents, and diseases caused by fatigue of workers. Due to the differences among workers and the diversity of assembly tasks, previous methods for task allocation is not efficient enough. This paper proposes a task reassignment method based on online fatigue detection. This method uses electroencephalography (EEG) and image processing data to detect workers' fatigue firstly, and then updates workers' fatigue status flag online according to the detection data, and then uses the improved NSGA-III algorithm proposed in this paper to obtain a new task assignment. The experiment results show that the multi-modal fatigue detection method proposed in this paper outperforms the one using single-modal data, and the improved NSGA-III algorithm is superior to the other two optimization algorithms in convergence speed and quality of the Pareto solution set.