Motion planning in dynamic environments, such as robotic warehouses, requires fast adaptation to frequent changes in obstacle poses. Traditional roadmap-based methods struggle in such settings, relying on inefficient reconstruction of a roadmap or expensive collision detection to update the existing roadmap. To address these challenges we introduce the Red-Green-Gray (RGG) framework, a method that builds on SPITE to quickly classify roadmap edges as invalid (red), valid (green), or uncertain (gray) using conservative geometric approximations. Serial RGG provides a high-performance variant leveraging batch serialization and vectorization to enable efficient GPU acceleration. Empirical results demonstrate that while RGG effectively reduces the number of unknown edges requiring full validation, SerRGG achieves a 2-9x speedup compared to the sequential implementation. This combination of geometric precision and computational speed makes SerRGG highly effective for time-critical robotic applications.

翻译：在动态环境（如机器人仓库）中进行运动规划时，需要快速适应障碍物位姿的频繁变化。传统的基于路线图的方法在此类场景中表现不佳，它们依赖低效的路线图重构或昂贵的碰撞检测来更新现有路线图。为解决这些挑战，我们提出了红-绿-灰（RGG）框架，该方法基于SPITE，通过保守的几何近似快速将路线图边分类为无效（红色）、有效（绿色）或不确定（灰色）。串行化RGG提供了一种高性能变体，利用批量序列化和向量化实现高效的GPU加速。实验结果表明，RGG有效减少了需要完整验证的未知边数量，而SerRGG相比顺序实现实现了2-9倍的加速。这种几何精度与计算速度的结合，使得SerRGG对时间敏感的机器人应用非常高效。