用轻量启发式方法快速判断动态环境中路径图边的有效性。
Quick Heuristic Validation of Edges in Dynamic Roadmap Graphs
- 提出红-绿-灰分类法，通过几何估算快速判断边状态。
- 在相近运行时间下，准确率高于经典Leven-Hutchinson方法。
- 适合实时路径规划中需频繁更新的动态环境场景。
本文针对机器人运动规划在非静态环境中的路网图调整问题，提出改进版SPITE方法——'红-绿-灰'范式，利用低成本启发式检查对节点与边的状态进行分类，实现快速半懒惰式路网更新。给定路网后，采用简单计算几何方法近似机器人扫掠体积，并执行懒惰碰撞检测，将部分边标记为无效（红）、有效（绿）或未知（灰）。实验初步对比了该方法与经典的Leven和Hutchinson技术，结果显示本方法在保持相当更新耗时的同时，具备更高准确率，且能正确识别无效边。
原文摘要 · Abstract (English)
In this paper we tackle the problem of adjusting roadmap graphs for robot motion planning to non-static environments. We introduce the "Red-Green-Gray" paradigm, a modification of the SPITE method, capable of classifying the validity status of nodes and edges using cheap heuristic checks, allowing fast semi-lazy roadmap updates. Given a roadmap, we use simple computational geometry methods to approximate the swept volumes of robots and perform lazy collision checks, and label a subset of the edges as invalid (red), valid (green), or unknown (gray). We present preliminary experimental results comparing our method to the well-established technique of Leven and Hutchinson, and showing increased accuracy as well as the ability to correctly label edges as invalid while maintaining comparable update runtimes.
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