# 复杂地形中用于轮腿机器人的机器人机身感知遍历风险图规划

> 原文链接：https://robotics.hellobike.com/papers/2026-08-17-2608-16433
> 频道：papers | 更新日期：2026-08-17 | 标签：arxiv, cs.RO
> 核心摘要：针对TRG忽略定向机身足迹的风险，RB-TRG以机身风险转移进行A*规划，并在扫描地形、MuJoCo和Go2-W实机上验证。

# 复杂地形中用于轮腿机器人的机器人机身感知遍历风险图规划

::: v-pre
> 原文链接：[Robot-Body-Aware Traversal Risk Graph Planning for Wheeled-Legged Robots in Complex Terrain](https://arxiv.org/abs/2608.16433)（arXiv 2608.16433 · 发布日期：2026-08-17）

**Robot-Body-Aware Traversal Risk Graph Planning for Wheeled-Legged Robots in Complex Terrain**

针对TRG忽略定向机身足迹的风险，RB-TRG以机身风险转移进行A*规划，并在扫描地形、MuJoCo和Go2-W实机上验证。

## 核心要点

- 原生TRG未按机器人定向机身足迹计费，转弯时可能漏判支撑丢失和机身干涉。
- RB-TRG沿图边和偏航扫掠采样矩形足迹，将机身风险特征并入转移代价。
- 在四个扫描地形和MuJoCo闭环试验中，成功率由51.5%升至68.5%，路径长增2.3%。
- Go2-W结合完整LiDAR导航栈完成部署，RB-TRG代码已在GitHub发布。

**验证范围与局限**：验证范围为四个扫描地形同图研究、成对闭环MuJoCo试验及Go2-W实机部署。

## 英文摘要 (Abstract)

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.

作者：Guo, Zhiqiao, Bichi Zhang, Sören Schwertfeger
:::

## 原始参考来源 (Sources)

- [arXiv 2608.16433：Robot-Body-Aware Traversal Risk Graph Planning for Wheeled-Legged Robots in Complex Terrain](https://arxiv.org/abs/2608.16433)