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. Our framework shows significant improvements in runtime and solution distance when compared with other sampling-based motion planners.

翻译：尽管近年来运动规划的效率和质量有所提升，但在部分已知环境中规划无碰撞且动力学可行的轨迹仍具挑战性，原因在于导航过程中随着未知障碍物的暴露而不断重规划不仅会带来显著的计算开销，还可能导致有害的振荡行为。为改善部分地图中运动规划的质量，本文提出一种框架，通过增强基于采样的运动规划，利用高层离散层和先验解来引导重规划过程中的运动树扩展，从而实现（i）更快的规划速度和（ii）更高的解一致性。与其他基于采样的运动规划器相比，本框架在运行时间和解路径距离方面展现了显著提升。