Aerial scans with unmanned aerial vehicles (UAVs) are becoming more widely adopted across industries, from smart farming to urban mapping. An application area that can leverage the strength of such systems is search and rescue (SAR) operations. However, with a vast variability in strategies and topology of application scenarios, as well as the difficulties in setting up real-world UAV-aided SAR operations for testing, designing an optimal flight pattern to search for and detect all victims can be a challenging problem. Specifically, the deployed UAV should be able to scan the area in the shortest amount of time while maintaining high victim detection recall rates. Therefore, low probability of false negatives (i.e., high recall) is more important than precision in this case. To address the issues mentioned above, we have developed a simulation environment that emulates different SAR scenarios and allows experimentation with flight missions to provide insight into their efficiency. The solution was developed with the open-source ROS framework and Gazebo simulator, with PX4 as the autopilot system for flight control, and YOLO as the object detector.

翻译：无人驾驶飞行器（UAV）的空中扫描正被更广泛地应用于从智慧农业到城市测绘的各个行业。能够充分利用这类系统优势的应用领域之一是搜索与救援（SAR）行动。然而，由于应用场景的策略和拓扑结构存在巨大差异，加之难以搭建用于测试的真实无人机辅助SAR行动系统，设计最优的飞行模式以搜索并检测所有受困者成为一个具有挑战性的问题。具体而言，部署的无人机应能在最短时间内扫描区域，同时保持较高的受害者检测召回率。因此，在此类场景中，低假阴性概率（即高召回率）比精确性更为重要。针对上述问题，我们开发了一个仿真环境，可模拟不同的SAR场景并支持飞行任务实验，以评估其效率。该解决方案基于开源ROS框架和Gazebo模拟器构建，采用PX4作为飞行控制自驾系统，并使用YOLO作为目标检测器。