发表机构
Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出面向移动机械臂的分层可移动障碍物导航框架，高层规划重定位并借助LLM处理依赖，底层通过离散接触推挤执行，实验验证了绕行、重定位及依赖场景下的可行导航操作。
AI 中文摘要
在存在大型可移动障碍物的环境中，仅绕行导航可能效率低下甚至不可行，而障碍物交互则需要推理导航收益、可行放置位置和可执行操作。我们提出了一种面向移动机械臂的分层可移动障碍物导航（NAMO）框架。在高层，规划器从参考路径中识别关键阻塞障碍物，并搜索同时满足几何、操作和下游导航约束的重新定位计划。当直接重新定位受到其他可移动物体阻碍时，有选择地调用大型语言模型（LLM）来推断辅助操作依赖关系，然后通过确定性几何规划进行验证。为了执行由此产生的重新定位目标，我们在盒状障碍物表面定义了离散接触模式，并基于位置和方向误差在线选择接触面和区域，通过接触切换实现直线、侧面和角落推挤。一种循环强化学习策略协调移动基座和机械臂，以跟踪工具中心点（TCP）目标，同时在持续推挤过程中保持末端执行器的可达性。仿真和真实机器人实验证明了在绕行、单个和多个障碍物重新定位以及依赖约束场景中可行的导航-操作，验证了该框架用于与大型不可抓取障碍物进行交互式导航的有效性。开源项目可在以下网址获取：此 https URL。
英文摘要
In environments with large movable obstacles, detour-only navigation can be inefficient or even infeasible, while obstacle interaction requires reasoning about navigation benefit, feasible placement, and executable manipulation. We present a hierarchical navigation among movable obstacles (NAMO) framework for mobile manipulators. At the high level, the planner identifies key blocking obstacles from reference paths and searches for relocation plans that jointly satisfy geometric, manipulation, and downstream navigation constraints. When direct relocation is hindered by other movable objects, a large language model (LLM) is selectively invoked to infer auxiliary manipulation dependencies, which are then verified by deterministic geometric planning. To execute the resulting relocation goals, we define discrete contact modes on the surfaces of box-shaped obstacles and select contact faces and regions online based on position and orientation errors, enabling straight, side, and corner pushing through contact switching. A recurrent reinforcement-learning policy coordinates the mobile base and manipulator to track tool center point (TCP) targets while preserving end-effector reachability during sustained pushing. Simulation and real-robot experiments demonstrate feasible navigation-manipulation in detour, single- and multi-obstacle relocation, and dependency-constrained scenarios, validating the framework for interactive navigation with large non-graspable obstacles. The open-source project is available at https://cloudytosunny.github.io/NAMO_DCPushing/.
