Autonomous agents face the challenge of coordinating multiple tasks (perception, motion planning, controller) which are computationally expensive on a single onboard computer. To utilize the onboard processing capacity optimally, it is imperative to arrive at computationally efficient algorithms for global path planning. In this work, it is attempted to reduce the processing time for global path planning in dynamically evolving polygonal maps. In dynamic environments, maps may not remain valid for long. Hence it is of utmost importance to obtain the shortest path quickly in an ever-changing environment. To address this, an existing rapid path-finding algorithm, the Minimal Construct was used. This algorithm discovers only a necessary portion of the Visibility Graph around obstacles and computes collision tests only for lines that seem heuristically promising. Simulations show that this algorithm finds shortest paths faster than traditional grid-based A* searches in most cases, resulting in smoother and shorter paths even in dynamic environments.

翻译：自主智能体面临多任务协调的挑战（感知、运动规划、控制器），这些任务在单个机载计算机上计算代价高昂。为最优利用机载处理能力，必须实现计算高效的全局路径规划算法。本研究尝试降低动态演化多边形地图中全局路径规划的处理时间。在动态环境中，地图可能无法长期保持有效。因此，在瞬息万变的环境中快速获取最短路径至关重要。为解决此问题，采用了现有快速路径发现算法——最小构造法。该算法仅探索障碍物周围可见性图的必要部分，并对启发式上具有前景的线段进行碰撞检测。仿真表明，在大多数情况下，该算法比传统基于网格的A*搜索更快找到最短路径，即使在动态环境中也能生成更平滑、更短的路径。