基于遗传算法的单元网格环境无人机集群路径规划系统
Genetic Algorithm Based System for Path Planning with Unmanned Aerial Vehicles Swarms in Cell-Grid Environments
AI总结：
针对含固定障碍物环境中的多无人机全覆盖路径规划问题，提出一种基于遗传算法的进化计算系统，可在无目标点和额外先验信息下最小化飞行时间与资源消耗。
摘要：
由于其作战优势，用于自主控制无人机（UAV）集群的路径规划方法正受到越来越多的关注。越来越多的场景现在需要对多架无人机进行自主控制，因为自主运行可以显著降低人力成本。此外，获得最优飞行路径可以降低能耗，从而为其他关键操作延长电池寿命。然而，这些场景中有许多涉及电线和树木等障碍物，这使路径规划变得复杂。本文提出了一种采用遗传算法的基于进化计算的系统，以在存在障碍物的环境中解决这一问题。所提出的方法旨在确保对具有固定障碍物的区域实现完全覆盖，例如在野外勘探任务中，同时无论地图大小或集群中的无人机数量如何，都能最大限度地缩短飞行时间。除所提供的地图外，不需要特定的目标点或先验信息。本研究中进行的实验使用了五张不同大小和障碍物密度的地图，以及一张无障碍物的对照地图，并采用了不同数量的无人机。结果表明，该方法能够在全图遍历过程中为所有无人机确定最优路径，从而最大限度地减少资源消耗。本文还给出了与其他最先进方法的比较分析，以突出所提方法的优势和潜在局限性。
英文摘要：
Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scenarios now require autonomous control of multiple UAVs, as autonomous operation can significantly reduce labor costs. Additionally, obtaining optimal flight paths can lower energy consumption, thereby extending battery life for other critical operations. Many of these scenarios, however, involve obstacles such as power lines and trees, which complicate Path Planning. This paper presents an evolutionary computation-based system employing genetic algorithms to address this problem in environments with obstacles. The proposed approach aims to ensure complete coverage of areas with fixed obstacles, such as in field exploration tasks, while minimizing flight time regardless of map size or the number of UAVs in the swarm. No specific goal points or prior information beyond the provided map is required. The experiments conducted in this study used five maps of varying sizes and obstacle densities, as well as a control map without obstacles, with different numbers of UAVs. The results demonstrate that this method can determine optimal paths for all UAVs during full map traversal, thus minimizing resource consumption. A comparative analysis with other state-of-the-art approach is presented to highlight the advantages and potential limitations of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
