提出新型足式机器人局部规划框架，实现复杂环境下的安全高效长距导航。
SCAN-Planner: Spatial Collision-Aware Local Planning for Route-Guided Long-Range Quadruped Navigation
- 用双圆柱体建模机器人，支持全身碰撞检测
- 在密集杂乱环境中实现平滑安全路径，支持爬楼梯
- 适合需要长距离自主导航的四足机器人应用
四足机器人正被期望在狭窄通道、杂乱室内场景和大规模三维非结构化环境中导航。现有局部规划器通常采用各向同性几何膨胀或依赖平面与高程图表示，导致在狭小空间中运动过于保守，并难以推理悬垂结构。本文提出SCAN-Planner，一种面向长距离四足导航的空间碰撞感知局部规划框架。采用航向感知的双圆柱体足印模型，通过稀疏查询膨胀3D占据图实现整机碰撞评估。引入投影A*搜索，在插值的地表跟随面上生成无碰撞引导路径，并通过抑制z梯度避免水平障碍物，保持垂直稳定性。针对大规模部署，采用以机器人为中心的滑动地图配合边界回退机制，提供高分辨率局部碰撞检测并可从局部死胡同中恢复。仿真与真实世界实验表明，SCAN-Planner在密集杂乱环境、三维非结构化场景、台阶穿越及长距离导航任务中均能生成安全、平滑且高效的轨迹。
原文摘要 · Abstract (English)
Quadruped robots are increasingly expected to navigate through narrow passages, cluttered indoor scenes, and large-scale 3D unstructured environments. Existing local planners commonly approximate the robot using isotropic geometric inflation or rely on planar and elevation-map representations, leading to conservative motion in tight spaces and limited reasoning about overhanging structures. This letter presents SCAN-Planner, a spatial collision-aware local planning framework for long-range quadruped navigation. A yaw-aware twin-cylinder footprint is used to model the elongated robot body, enabling whole-body collision evaluation through sparse queries in an inflated 3D occupancy map. We further introduce a projected A* search that generates collision-free guidance on an interpolated ground-following surface, with z-gradient suppression to avoid obstacles horizontally while maintaining vertical stability. For large-scale deployment, a robot-centric sliding map with boundary fallback provides high-resolution local collision checking and recovery from local dead ends. Simulation and real-world experiments demonstrate that SCAN-Planner generates safe, smooth, and efficient trajectories in dense clutter, 3D unstructured scenes, stair traversal, and long-range navigation tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。