导航还是重新布置？在未知环境中穿越可移动障碍物的规划

中文摘要 AI 辅助

传统的机器人规划方法寻求到达目标的无碰撞路径，但当所有路径都被阻塞时，这些方法会失效。在这些情况下，机器人必须确定哪些物体需要重新布置、以何种顺序布置以及将它们放置在哪里以清理出一条路径——这个问题被称为“可移动障碍物中的导航”（NAMO）。大多数NAMO规划器假设环境已知，而现有的针对未知环境的方法通常局部地推理重新布置，无法规划相互依赖的重新布置序列。我们考虑在通过机载感知揭示的未知环境中的NAMO问题，其中机器人必须决定阻塞的路线是否需要重新布置，或者是否存在穿过未探索空间的可行路径。我们提出了一个在线框架，通过使用最短路径在导航和重新布置之间进行选择来解决这种模糊性，这些最短路径将发现的可移动障碍物视为障碍物或可移除物。导航依赖现有的运动规划器，而重新布置使用一种基于采样的方法，与现有的针对未知环境的方法不同，该方法搜索相互依赖的重新布置序列，并使用LLM来偏置采样。数值实验证明了该方法在需要相互依赖重新布置的杂乱环境中的可扩展性，并且相对于现有基线提高了规划质量。

英文摘要

Conventional robot planning methods seek collision-free paths to a goal but fail when all paths are blocked. In these cases, the robot must determine which objects to relocate, in what order, and where to place them to clear a path---a problem known as Navigation Among Movable Obstacles (NAMO). Most NAMO planners assume a known environment, while existing approaches for unknown environments typically reason locally about relocations and cannot plan interdependent relocation sequences. We consider NAMO in unknown environments revealed through onboard sensing, where the robot must decide whether a blocked route requires relocation or a feasible path may exist through unexplored space. We propose an online framework that addresses this ambiguity by selecting between navigation and relocation using shortest paths that treat discovered movable objects as obstacles or as removable. Navigation relies on existing motion planners, while relocation uses a sampling-based approach that, unlike existing approaches for unknown environments, searches over \textit{interdependent} relocation sequences and uses an LLM to bias sampling. Numerical experiments demonstrate scalability to cluttered environments requiring interdependent relocations and improved plan quality over existing baselines.

发表机构

• Washington University in St. Louis（圣路易斯华盛顿大学）

机构由 AI 辅助整理，请以论文原文为准。