让轮足机器人在复杂地形中更安全地规划路径。
Robot-Body-Aware Traversal Risk Graph Planning for Wheeled-Legged Robots in Complex Terrain
- 基于机器人本体形状和朝向，动态计算路径风险。
- 路径成功率从51.5%提升至68.5%，几何误差降低。
- 适合需要高可靠性移动的轮足机器人系统开发者。
通行风险图（TRG）为全局导航提供紧凑、地形感知的表示，但其原始成本基于圆形节点邻域和边对齐的地形区域，而非机器人的定向本体轮廓。对于轮足机器人，这种抽象可能忽略局部支撑丧失和本体-地形干涉，尤其是在转弯时。本文提出机器人本体感知的TRG规划（RB-TRG），在稀疏TRG表示基础上，将边级地形风险搜索升级为航向与转向感知的本体风险转移。沿图边采样有向矩形本体轮廓及偏航扫掠，测量纵向支撑变化、横向倾斜、地形干涉以及暴露于不可信地图区域的程度。引入均值与上尾特征到转移代价中，通过A*算法在有序节点对状态上最小化累积代价，保持原有TRG构建与规划接口。我们在四个扫描地形环境的同图研究中评估，并在成对闭环MuJoCo实验中验证。RB-TRG显著降低三项核心几何本体位置指标，端到端成功率由51.5%提升至68.5%，平均路径长度增加2.3%。在Go2-W上的部署进一步展示了集成完整激光雷达导航栈的性能，在IEEE ICRA 2026轮式机器人挑战赛中获最佳自主与最佳移动奖。代码已开源：https://github.com/ZhiqiaoGuo/RB-TRG。
原文摘要 · Abstract (English)
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.
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