Optimal UAV Route Planning for Coverage Search of Stationary Target in River

In this brief, we focus on the offline route planning of unmanned aerial vehicle (UAV) for the coverage search mission in a river region. Given the prior likelihood distribution of area importance, this brief aims to generate UAV feasible routes which maximize the cumulative probability of finding a single and stationary target within the required time. First, Gaussian mixture model is used to approximate the prior likelihood distribution, and several river segments with high detection probability corresponding to Gaussian components can be extracted. With the consideration of quantified factors, the river subregions are prioritized by the approximation insertion method and then allocated to UAVs. Moreover, to meet the terminal time constraint, the so-called positive/negative greedy method is proposed to expand or contract waypoints. Finally, the performance of our proposed algorithm is evaluated by simulations on a real river map, and the results verify its good performance in various scenarios.