Robust Task Planning for Assembly Lines with Human-Robot Collaboration

Abstract

Efficient and robust task planning for a human-robot collaboration (HRC) system remains challenging. The human-aware task planner needs to assign jobs to both robots and human workers so that they can work collaboratively to achieve better time efficiency. However, the complexity of the tasks and the stochastic nature of the human collaborators bring challenges to such task planning. To reduce the complexity of the planning problem, we utilize the hierarchical task model which explicitly captures the sequential and parallel relationships of the task. To account for human-induced uncertainties, we model human movements with the sigma-lognormal functions. A human action model adaptation scheme is applied during run-time and it provides a measure for modeling the human-induced uncertainties. We propose a sampling-based method to estimate the uncertainties in human job completion time. Next, we propose a robust task planner, which formulates the planning problem as a robust optimization problem by considering the task structure and the uncertainties. We conduct simulations of a robot arm collaborating with a human worker in an electronics assembly setting. The results show that our proposed planner, compared to the baseline planner, can reduce task completion time when human-induced uncertainties occur.