Path planning of UAVs based on improved Clustering Algorithm and Ant Colony System Algorithm

This paper studies the path planning problem of multi-UAVs with multiple missions under complicated constraints, and proposes a new approach to provide optimal paths for each UAV such that the task completion time would be minimized. First, with the model of UAVs, we analyze the object function and travelling constraints of the path planning problem. Then, by considering the limit of the maximum yaw angle of UAVs, we propose an efficient approach to solve the path planning problem by combining the improved Clustering by Fast Search and Find of Density Peaks algorithm (CFSDP) and ant colony system (ACS) algorithm together. The propose approach not only helps UAVs in covering the cruise valid areas, but also finds the shortest tasks completion time for each UAV to perform the searching task. Finally, we use simulation experiments randomly generated targets to verify the effectiveness of the proposed approach.