arXiv:2505.09833cs.ROcs.LG2025-05被引 1

用机械臂推移障碍物,让多机器人高效通行

Learning Rock Pushability on Rough Planetary Terrain

  • 用机械臂推障碍物代替绕行,提升路径效率
  • 结合视觉与力反馈判断能否推动,实测有效
  • 适合月球火星等需长期部署的无人环境

在非结构化环境中,传统导航多采用避障策略,路径规划需绕行并返回原路线,导致重复路径效率低下。本文提出一种新方法:利用搭载于移动机器人的机械臂主动操作障碍物。通过融合外部视觉与本体感知反馈,评估障碍物可推性,实现其重定位而非避让。初步视觉估计考虑障碍物与地面特性,而推移可行性模块则以机械臂交互时的力反馈为引导信号。该方法旨在提升多机器人长期使用路径的效率,减少在需要自主基础设施建设的环境(如月球或火星表面)中,整个机群的总耗时。

原文摘要 · Abstract (English)

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.

机器人导航障碍推移月球探测

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