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.

翻译：本文针对非静态环境下机器人运动规划的路图图调整问题展开研究。我们提出了"红-绿-灰"范式——一种改进SPITE方法的技术，能够通过低成本的启发式检查对节点和边的有效性状态进行分类，实现快速的半惰性路图更新。给定路图后，我们采用简单的计算几何方法近似机器人的扫掠体并执行惰性碰撞检测，将边的子集标记为无效（红色）、有效（绿色）或未知（灰色）。我们展示了与Leven和Hutchinson经典方法的初步对比实验结果，表明本方法在保持相当更新运行时间的同时，不仅提高了标注准确性，还能正确识别无效边。