迈向路径创造性导航：通过具身交互实现机器人导航

中文摘要 AI 辅助

在杂乱且受限环境中的自主导航通常假设环境固定，仅在现有自由空间内搜索路径。然而，路线可能被铰接结构、可移动物体或行人阻挡，因此到达目标可能需要机器人与环境进行适当的具身交互。本文提出路径创造性导航（Path-Creative Navigation, PCN），这是一种机器人通过协调运动与具身交互恢复自由空间的导航范式，并针对一类PCN任务提出了无地图框架。该视觉-激光雷达-里程计（vision-LiDAR-odometry）框架通过统一导航系统整合局部观测、障碍物几何形状及机器人状态估计，为导航与交互提供稳定的语义和几何信息。可通行性感知决策方法利用视觉和激光雷达距离信息，判断障碍物是否可操作以及是否可安全绕行，使机器人仅在必要时进行交互，同时支持铰接结构推动、可移动物体推动、避障及响应行人请求。我们设计了仿真场景，并在仿真及两项仅靠传统导航无法完成的真实任务中评估了不同方法。结果表明，我们的框架通过在杂乱受限环境中协调导航与交互，比基线方法实现了更高的任务完成率。代码可在以下网址获取：{ this https URL }

英文摘要

Autonomous navigation in cluttered and constrained environments typically assumes a fixed environment and searches only for paths within existing free space. However, a route can be blocked by an articulated structure, a movable object, or a pedestrian, and reaching the goal may therefore require appropriate embodied interaction with the environment. This paper formulates Path-Creative Navigation (PCN), a navigation paradigm in which the robot recovers free space by coordinating locomotion and embodied interaction, and addresses one class of PCN tasks with a mapless framework. The vision-LiDAR-odometry framework integrates local observations, obstacle geometry, and robot-state estimates through a unified navigation system, providing stable semantic and geometric information for navigation and interaction. The traversability-aware decision method utilizes visual and LiDAR distance information to determine whether a blockage is actionable and whether it can be safely bypassed, enabling the robot to interact only when necessary while supporting articulated structure pushing, movable object pushing, obstacle avoidance, and pedestrian requests. We design simulation scenarios and evaluate different methods in both simulation and two real-world tasks that cannot be completed by conventional navigation alone without embodied interaction. These results demonstrate that our framework achieves higher task completion than the baseline methods by coordinating navigation and interaction in cluttered and constrained environments. Code is available at:{https://anonymous.4open.science/r/path-creative-navigation/}.

发表机构

• School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）
• College of Information Science and Technology, Eastern Institute of Technology（东方理工大学信息科学与技术学院）
• School of Mechanical and Power Engineering, Zhengzhou university（郑州大学机械与动力工程学院）
• University of Waterloo（滑铁卢大学）

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