用反应扩散系统生成更高效、更优的机器人路径规划地图
GSRM: Building Roadmaps for Query-Efficient and Near-Optimal Path Planning Using a Reaction Diffusion System
- 基于灰-斯科特反应扩散与三角剖分构建道路图
- 在狭窄通道环境下仍保持良好连通性，查询效率高
- 适合需快速规划且路径近最优的机器人场景
移动机器人在医疗、酒店及仓储自动化等场景中常依赖道路图（即边代表安全运动的图）进行导航。环境通常为准静态，只需一次性构建道路图即可支持后续所有规划查询。道路图需与图搜索算法配合，确保路径可行、近似最优且计算高效。本文提出GSRM方法，基于灰-斯科特反应扩散系统与Delaunay三角剖分构建道路图。该方法生成顶点分布均匀、连接性强的道路图，尤其在具有挑战性的狭窄通道环境中表现优异。实验对比了8连通网格、概率路图（PRM、SPARS2）和优化路图（ORM），结果表明GSRM始终生成连通性更好、查询效率更高、解路径更短的道路图。
原文摘要 · Abstract (English)
Mobile robots frequently navigate on roadmaps, i.e., graphs where edges represent safe motions, in applications such as healthcare, hospitality, and warehouse automation. Often the environment is quasi-static, i.e., it is sufficient to construct a roadmap once and then use it for any future planning queries. Roadmaps are typically used with graph search algorithm to find feasible paths for the robots. Therefore, the roadmap should be well-connected, and graph searches should produce near-optimal solutions with short solution paths while simultaneously be computationally efficient to execute queries quickly. We propose a new method to construct roadmaps based on the Gray-Scott reaction diffusion system and Delaunay triangulation. Our approach, GSRM, produces roadmaps with evenly distributed vertices and edges that are well-connected even in environments with challenging narrow passages. Empirically, we compare to classical roadmaps generated by 8-connected grids, probabilistic roadmaps (PRM, SPARS2), and optimized roadmap graphs (ORM). Our results show that GSRM consistently produces superior roadmaps that are well-connected, have high query efficiency, and result in short solution paths.
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