Problem
Research questions and friction points this paper is trying to address.
Navigation Among Movable Obstacles
unknown environments
interdependent relocation sequences
Navigate or Relocate? Planning Among Movable Obstacles in Unknown Environments
🤖 AI Summary
研究解决了未知环境中机器人导航与可移动障碍物重定位的问题，提出了一种在线框架，通过最短路径选择导航或重定位策略。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
online framework
interdependent relocation sequences
unknown environments
onboard sensing
LLM for sampling bias
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