用风险图优化复杂地形路径规划，兼顾安全与效率。
TRG-planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation
- 构建地形可通行性风险图，节点表征稳定性，边表征路径风险。
- 实现在大规模环境中的实时规划，比传统方法更安全且路径更短。
- 已用于四足机器人竞赛夺冠方案，适合复杂场景自主导航研究者。
非结构化环境（如山地、洞穴、施工区或灾害区域）因地形不规则，对自主导航构成挑战。本文提出一种基于遍历风险图（TRG）的安全高效路径规划方法，该图表示能反映地形几何可通行性：节点代表地形的稳定性和可达性，边代表相对风险加权的路径候选。TRG采用波前传播方式构建并分层管理，支持在大规模环境中实时规划。此外，我们在TRG上构建图优化问题，使机器人优先选择安全且短的路径。实验表明，本方法在安全性、路径距离效率和处理速度方面均优于传统方法。已在真实四足机器人上验证，并作为DreamSTEP团队在ICRA 2023四足机器人挑战赛中夺冠的自主导航框架的全局规划器。项目主页见 https://trg-planner.github.io 。
原文摘要 · Abstract (English)
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 .
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