发表机构
School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种无预定义参考路径的自适应多尺度凸集图框架，用于生成同伦感知安全走廊，经数值与硬件实验验证，其图构造高效、轨迹性能稳定，同伦感知轨迹更短且能应对未知障碍物。
AI 中文摘要
生成安全走廊对于机器人无碰撞运动规划至关重要，但现有大多数方法依赖预定义参考路径，这会偏向走廊几何结构，并隐式限制可探索的同伦类。我们提出一种基于凸集图（GCS）的无参考路径走廊生成框架，直接将走廊构造为凸集序列，使走廊结构从自由空间表示中自然形成，而非来自引导路径。为推理走廊间的相似性，我们将基于可见性的变形从路径扩展至凸集序列，实现拓扑冗余走廊的融合，同时保留不同的可行方案。为克服现有基于静态全局分解的GCS方法适应性有限的问题，我们进一步开发自适应多尺度GCS，其中基于采样的细粒度图支持局部更新，基于可见性的粗粒度图实现紧凑的全局探索。两个尺度维持拓扑一致性，允许在环境不确定性下进行增量更新，无需完全重构图。数值实验对GCS构造、走廊生成、同伦感知探索及局部更新进行了表征，结果显示图构造高效、轨迹级性能稳定，且同伦感知轨迹的持续时间短于现有基线。地面机器人与空中机器人的硬件实验，包括利用机载定位的部署，进一步验证了该框架在平移及未知障碍物场景下的有效性。
英文摘要
Generating safe corridors is essential for collision-free robotic motion planning, yet most existing methods rely on predefined reference paths, which bias corridor geometry and implicitly limit the homotopy classes that can be explored. We propose a reference-path-free corridor generation framework on graphs of convex sets (GCS) that constructs corridors directly as sequences of convex sets, allowing corridor structure to emerge from the free-space representation rather than from a guiding path. To reason about similarity among corridors, we extend visibility-based deformation from paths to convex-set sequences, enabling the fusion of topologically redundant corridors while preserving distinct alternatives. To overcome the limited adaptability of existing GCS methods based on static global decompositions, we further develop an adaptive multi-scale GCS, in which a sampling-based fine-scale graph supports localized updates and a visibility-based coarse-scale graph enables compact global exploration. The two levels maintain topological consistency, allowing incremental updates without full graph reconstruction under environmental uncertainty. Numerical experiments characterize GCS construction, corridor generation, homotopy-aware exploration, and local updates, showing efficient graph construction, stable trajectory-level performance, and shorter-duration homotopy-aware trajectories than existing baselines. Hardware experiments on ground and aerial robots, including deployment with onboard localization, further validate the framework under translated and previously unknown obstacles.
Comments8 pages, 8 figures. Accepted for publication in IEEE Robotics and Automation Letters (RA-L)
