Guided Sampling-Based Motion Planning with Dynamics in Unknown Environments

Abstract-- Despite recent progress improving the efficiency and quality of motion planning, planning collision-free and dynamically-feasible trajectories in partially-mapped environments remains challenging, since constantly replanning as unseen obstacles are revealed during navigation both incurs significant computational expense and can introduce problematic oscillatory behavior. To improve the quality of motion planning in partial maps, this paper develops a framework that augments sampling-based motion planning to leverage a high-level discrete layer and prior solutions to guide motion-tree expansion during replanning, affording both (i) faster planning and (ii) improved solution coherence. A trajectory in a partially-mapped environment planned by our framework. Videos of solutions obtained by our framework on this and other scenes used in the experiments can be found at tinyurl.com/47ct55s6 This task is made challenging by the resulting in problematic oscillatory behavior as the robot presence of obstacles during deployment, which requires iteratively navigates, reveals structure, and replans.