UAV coverage path optimization for collecting fine-grained distribution of air samples

With the advent of smart systems, monitoring the level of air pollution in cities and industrial plants has become an important issue, necessitating the collection of air samples on a regular basis and at various altitudes. As a result, using a UAV for this task will aid in providing an accurate and timely assessment of the air components of pollutants. However, due to the limited power onboard, the UAV must be provided with an optimized coverage path planning. This paper introduces a grid-based coverage path planner that finds the shortest and most energy-efficient path between sampling points. The proposed method ensures that the UAV only passes one way-point and avoids inefficient back-and-forth. Furthermore, the coverage path is limited to take into account the return to launch point in order to allow the UAV to recharge and reload. Two optimization algorithms were compared and tested in real-world flight environments, and the results showed the best paths.