Robust Navigation Execution by Planning in Belief Space

We consider robot navigation in environments consisting of a known static map, but where dynamic obstacles of varying and unknown lifespans appear and disappear over time. We describe a roadmap-based formulation of the problem that takes the sensing and transition uncertainty into account, and an efficient online planner for this problem. The planner displays behaviors such as persistence and obstacle timeouts that would normally be hardcoded into an executive. It is also able to make inferences about obstacle types even with impoverished sensors. We present empirical results on simulated domains and on a PR2 robot.