  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.
