A PRM-based motion planner for dynamically changing environments

This paper presents a path planner for robots operating in dynamically changing environments with both static and moving obstacles. The proposed planner is based on probabilistic path planning techniques and it combines techniques originally designed for solving multiple-query and single-query problems. The planner first starts with a preprocessing stage that constructs a roadmap of valid paths with respect to the static obstacles. It then uses lazy-evaluation mechanisms combined with a single-query technique as local planner in order to rapidly update the roadmap according to the dynamic changes. This allows to answer queries quickly when the moving obstacles have little impact on the free-space connectivity. When the solution can not be found in the updated roadmap, the planner initiates a reinforcement stage that possibly results into the creation of cycles representing alternative paths that were not already stored in the roadmap. Simulation results show that this combination of techniques yields to efficient global planner capable of solving with a real-time performance problems in geometrically complex environments with moving obstacles