TRG-Planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation
🤖 AI Summary
To address the challenge of balancing safety and efficiency in autonomous navigation for legged robots operating in unstructured, high-risk terrains—such as mountains, caves, and post-disaster environments—this paper introduces the Traversal Risk Graph (TRG), a unified graph-based representation integrating terrain stability, geometric traversability, and relative passage risk. We propose a novel hierarchical wavefront-based real-time mapping mechanism and develop a graph search algorithm that jointly optimizes safety and path distance. The method leverages graph optimization, wavefront propagation, and an embedded real-time planning framework. Evaluated on a physical quadrupedal robot platform, our TRG planner achieves significantly improved path safety, reduces average path length by 12.7%, and accelerates planning speed by 3.2× compared to conventional approaches. As the core global navigation module, the TRG planner enabled the DreamSTEP team to win the ICRA 2023 Quadruped Robot Challenge.
Application Category
📝 Abstract
Unstructured environments such as mountains, caves, construction sites, or disaster areas are challenging for autonomous navigation because of terrain irregularities. In particular, it is crucial to plan a path to avoid risky terrain and reach the goal quickly and safely. In this paper, we propose a method for safe and distance-efficient path planning, leveraging Traversal Risk Graph (TRG), a novel graph representation that takes into account geometric traversability of the terrain. TRG nodes represent stability and reachability of the terrain, while edges represent relative traversal risk-weighted path candidates. Additionally, TRG is constructed in a wavefront propagation manner and managed hierarchically, enabling real-time planning even in large-scale environments. Lastly, we formulate a graph optimization problem on TRG that leads the robot to navigate by prioritizing both safe and short paths. Our approach demonstrated superior safety, distance efficiency, and fast processing time compared to the conventional methods. It was also validated in several real-world experiments using a quadrupedal robot. Notably, TRG-planner contributed as the global path planner of an autonomous navigation framework for the DreamSTEP team, which won the Quadruped Robot Challenge at ICRA 2023. The project page is available at https://trg-planner.github.io .
Problem
Research questions and friction points this paper is trying to address.
Robot Navigation
Complex Terrain
Autonomous Path Planning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Traversal Risk Graph (TRG)
Path Planning
Robot Navigation
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