A Multi-target Multi-clustering Trajectory Optimization Strategy for Multi-UAV

Unmanned Aerial Vehicles (UAVs) play an important role in providing coverage for communication terminals during disaster relief efforts. This article investigates a path planning algorithm for UAVs that takes into account communication, the number of UAVs, and constraints on their journey. A quantity estimation algorithm (QEA) is proposed to estimate the quantity of UAVs used for communication tasks based on the K-means algorithm. The problem can be abstracted as the traveling salesman problem (TSP) in order to minimize the UAV cruise time as much as possible. Optimal paths are then solved using ant colony optimization (ACO). The planning trajectory obtained using single clustering and ACO cannot meet the operational limitations of UAVs. Therefore, a multi-target multi-clustering optimization algorithm is proposed for UAVs based on the K-means algorithm. In addition, the efficiency of cruising can be improved through multiple effective cluster analyses by utilizing the proposed center path and second cluster path methods. These results indicate that the proposed algorithms can improve the likelihood of successfully achieving multiple targets during UAV cruising.