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.

翻译：无人机群自主控制的路径规划方法因其操作优势正日益受到关注。随着越来越多场景需要多无人机自主控制，自主运行能显著降低人力成本。此外，获取最优飞行路径可降低能耗，从而延长其他关键任务的电池续航时间。然而，许多场景中存在电线、树木等障碍物，使路径规划复杂化。本文提出一种基于进化计算的系统，采用遗传算法解决含障碍物环境中的该问题。所提方法旨在确保对固定障碍物区域（如野外勘探任务）的完全覆盖，同时无论地图规模或集群中无人机数量如何，均能最小化飞行时间。除提供的地图外，无需特定目标点或先验信息。本研究使用五幅不同尺寸和障碍物密度的地图以及无障碍对照地图，在不同无人机数量下进行实验。结果表明，该方法能在全图遍历过程中为所有无人机确定最优路径，从而最小化资源消耗。通过与其他前沿方法的对比分析，突显了所提方法的优势与潜在局限性。