進むか退けるか？未知環境における可動障害物を考慮した計画
Navigate or Relocate? Planning Among Movable Obstacles in Unknown Environments
未知環境で経路が塞がれたとき、ロボットが探索と障害物の移動をどう選ぶかをオンラインで判断し、LLMで効率的に移動順序を計画する手法を提案した論文。
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著者: Yuqing Zhang, Haoyu Zhu, Yiannis Kantaros
分類: cs.RO
原文アブストラクト
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.