Optimal Path for Unmanned Aerial Vehicles Based on Improved Ant Colony Algorithm
Min Li, Shifang Zhang · 2025
Aiming at the problems of complex and variable terrain, sparse road network and wide distribution of distribution points in some areas of China, considering the different performance of UAVs, flight cost and urgency of distribution points, an improved ant colony algorithm incorporating local search strategy is proposed to achieve efficient logistics and distribution and enhance the utilization of resources. Under this algorithm, the latitude and longitude coordinates of the dataset are firstly cleaned and converted into the universal transverse Mercator projection coordinate system (UTM), and then the logistics points are clustered by using K-means++ clustering analysis, and the optimal paths of the UAVs under different clustering centers and between the clustering centers are determined with the help of the improved ACO algorithm, and the delivery paths of the UAVs are compared with different numbers of cluster centers K, so as to determine the optimal picking and delivery paths, and to determine the optimal picking and distribution paths. paths under different number of cluster centers K, so as to determine the optimal pickup and delivery routes. The simulation results show that the proposed optimal path algorithm for joint bus-drone pickup can jump out of the local optimum and improve the solving efficiency of path planning, and the efficiency of the algorithm is improved by 3.60%, with the lowest total cost of 1457.82 RMB.