Reactive planning under uncertainty among moving obstacles

This paper addresses the problem of navigation of complex systems in dynamic environments under uncertainty. Such environments impose a hard real time constraint. However, computing a complete motion to the goal within a limited time is impossible to achieve in most real situations. The Partial Motion Planning (PMP) approach presented in [1] is used to address the planning problem. However, for real applications, it is important to take into account uncertainty. In this paper, we present an extension to the PMP that accounts for uncertainty in order to plan trajectories robust to the robot’s errors. We show that PMP framework is highly suitable to account for these constraints and present original simulation results of robust trajectories for a car-like robot evolving among moving obstacles.