Robot-Body-Aware Traversal Risk Graph Planning for Wheeled-Legged Robots in Complex Terrain
2026-08-17 • Robotics
Robotics
AI summaryⓘ
The authors improve a navigation method called Traversal Risk Graphs (TRGs) to better suit wheeled-legged robots. Their approach, RB-TRG, considers the actual shape and orientation of the robot's body when planning paths, especially during turns, to avoid slipping or hitting rough terrain. They test RB-TRG on scanned environments and simulations, showing it helps the robot succeed more often with only a slight increase in path length. They also demonstrate its real-world use on a robot with successful outcomes in a competition. The authors provide the software for others to use.
Traversal Risk Graphswheeled-legged robotsterrain-aware navigationrobot footprintA* searchbody-terrain interactionMuJoCo simulationLiDAR navigationpath planningrobot mobility
Authors
Zhiqiao Guo, Bichi Zhang, Sören Schwertfeger
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.