Unmanned aerial vehicles (UAVs) are increasingly utilized in global search and rescue efforts, enhancing operational efficiency. In these missions, a coordinated swarm of UAVs is deployed to efficiently cover expansive areas by capturing and analyzing aerial imagery and footage. Rapid coverage is paramount in these scenarios, as swift discovery can mean the difference between life and death for those in peril. This paper focuses on optimizing flight path planning for multiple UAVs in windy conditions to efficiently cover rectangular search areas in minimal time. We address this challenge by dividing the search area into a grid network and formulating it as a mixed-integer program (MIP). Our research introduces a precise lower bound for the objective function and an exact algorithm capable of finding either the optimal solution or a near-optimal solution with a constant absolute gap to optimality. Notably, as the problem complexity increases, our solution exhibits a diminishing relative optimality gap while maintaining negligible computational costs compared to the MIP approach.

翻译：无人机（UAV）在全球搜救行动中的应用日益广泛，显著提升了作业效率。在这类任务中，无人机集群协同部署，通过采集与分析航拍图像及影像资料，实现对广阔区域的高效覆盖。快速覆盖至关重要，因为即时发现往往直接决定受困人员的生死存亡。本文聚焦于在多风环境下优化多无人机飞行路径规划，以最短时间高效覆盖矩形搜索区域。我们通过将搜索区域划分为网格网络，并将其建模为混合整数规划（MIP）问题来应对这一挑战。研究提出了目标函数的精确下界，并设计了一种精确算法，该算法能够求得最优解或与最优解保持恒定绝对差距的近似最优解。值得注意的是，随着问题复杂度的增加，我们的解在保持相对于MIP方法可忽略的计算成本的同时，展现出逐渐缩小的相对最优性差距。