在粗糙行星地形上学习岩石可推动性
Learning Rock Pushability on Rough Planetary Terrain
AI总结：
本文提出在非结构化环境中利用移动机器人搭载的机械臂评估并推动障碍物，通过视觉与力反馈估计推动可供性，以重新定位障碍物替代规避，从而提升多智能体长期路径效率。
摘要：
在非结构化环境中的移动导航背景下，主要方法是对障碍物进行规避。现有的路径规划算法依赖于在不确定的持续时间内偏离预定路径，并在空间上越过障碍物后返回路径上最近的点。然而，在将被多个智能体重复使用的路径上规避障碍物，可能会妨碍长期效率，并导致对主动路径规划系统的持续依赖。在本研究中，我们提出了一种在非结构化环境中进行移动导航的替代方法，利用安装在移动机器人顶部的机器人机械臂的操控能力。我们提出的框架整合了外部感知和本体感知反馈，以评估障碍物的推动可供性，从而促进对障碍物的重新定位而非规避。虽然我们的初步视觉估计考虑了障碍物及其所依赖表面的特征，但推动可供性估计模块利用通过机器人机械臂与障碍物交互获得的力反馈作为引导信号。我们的导航方法的目标是，在自主基础设施建设至关重要的环境中（例如月球或火星表面），通过减少车队在环境中花费的总时间，提高多个智能体在长时间内所使用路径的效率。
英文摘要：
In the context of mobile navigation in unstructured environments, the predominant approach entails the avoidance of obstacles. The prevailing path planning algorithms are contingent upon deviating from the intended path for an indefinite duration and returning to the closest point on the route after the obstacle is left behind spatially. However, avoiding an obstacle on a path that will be used repeatedly by multiple agents can hinder long-term efficiency and lead to a lasting reliance on an active path planning system. In this study, we propose an alternative approach to mobile navigation in unstructured environments by leveraging the manipulation capabilities of a robotic manipulator mounted on top of a mobile robot. Our proposed framework integrates exteroceptive and proprioceptive feedback to assess the push affordance of obstacles, facilitating their repositioning rather than avoidance. While our preliminary visual estimation takes into account the characteristics of both the obstacle and the surface it relies on, the push affordance estimation module exploits the force feedback obtained by interacting with the obstacle via a robotic manipulator as the guidance signal. The objective of our navigation approach is to enhance the efficiency of routes utilized by multiple agents over extended periods by reducing the overall time spent by a fleet in environments where autonomous infrastructure development is imperative, such as lunar or Martian surfaces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
