发表机构
ShanghaiTech University; Key Laboratory of Intelligent Perception and Human-Machine Collaboration（上海科技大学; 智能感知与人机协同重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对轮腿式机器人复杂地形导航的TRG规划缺陷，提出RB-TRG方法，提升了导航成功率，获IEEE ICRA 2026挑战赛奖项，代码已开源。
AI 中文摘要
遍历风险图（TRG）为全局导航提供了紧凑的、感知地形的表示，但原生TRG代价是基于圆形节点邻域和边对齐的地形区域计算的，而非机器人的定向本体 footprint（ footprint 指机器人占地轮廓）。对于轮腿式机器人，这种抽象会遗漏部分支撑损失和本体-地形干涉，尤其在转向时。我们提出机器人本体感知TRG规划（RB-TRG），它基于稀疏TRG表示，将边级地形风险搜索提升为航向和转向感知的本体风险转换。沿图边和偏航扫描采样定向矩形 footprint，以测量纵向支撑变化、横向倾斜、地形干涉以及对不可信地图区域的暴露程度。均值和上尾特征被纳入转换代价，A*算法在有序节点对状态上最小化累积代价，同时保留TRG的构建及其规划接口。我们在四个扫描地形环境的同图研究和配对闭环MuJoCo试验中评估RB-TRG。RB-TRG降低了三个核心几何本体放置指标，将端到端成功率从51.5%提升至68.5%，同时平均路径长度增加2.3%。Go2-W部署进一步展示了RB-TRG结合完整LiDAR导航栈的效果，其在IEEE ICRA 2026腿式机器人挑战赛中获得最佳自主和最佳移动性奖项。RB-TRG的代码已在此URL发布。
英文摘要
Traversal Risk Graphs (TRGs) provide a compact, terrain-aware representation for global navigation, but native TRG costs are computed over circular node neighborhoods and edge-aligned terrain regions rather than the robot's oriented body footprint. For wheeled-legged robots, this abstraction can miss partial support loss and body-terrain interference, especially during turns. We present Robot-Body-Aware TRG planning (RB-TRG), which builds on the sparse TRG representation and lifts edge-wise terrain-risk search to heading- and turn-aware body-risk transitions. An oriented rectangular footprint is sampled along graph edges and yaw sweeps to measure longitudinal support variation, lateral inclination, terrain interference, and exposure to untrusted map regions. Mean-and-upper-tail features are incorporated into transition costs, whose accumulated value is minimized by A* over ordered node-pair states, preserving TRG construction and its planning interface. We evaluate RB-TRG in a same-graph study on four scanned terrain environments and in paired closed-loop MuJoCo trials. RB-TRG reduces the three core geometric body-placement metrics and increases end-to-end success from 51.5% to 68.5%, while increasing mean path length by 2.3%. A Go2-W deployment further demonstrates RB-TRG with a full LiDAR navigation stack, which received the Best Autonomy and Best Mobility awards at the IEEE ICRA 2026 Legged Robot Challenges. The code for RB-TRG is released at https://github.com/ZhiqiaoGuo/RB-TRG.
