  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. We model human movements with the sigma-lognormal
functions to account for human-induced uncertainties. 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 human job completion time uncertainties. 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 can reduce task
completion time when human-induced uncertainties occur compared to the baseline
planner.
