基于优化蚁群算法的无人机校园物流路径规划研究

Research on UAV Campus Logistics Path Planning Based on Optimized Ant Colony Algorithm

摘要: 近年来，校园快递业务量急剧攀升，传统配送模式效率低下、成本高昂的问题日益凸显。本文在蚁群算法的基础上提出了四重优化机制，开展无人机校园物流配送模拟实验，通过构建蚁群算法的求解模型，以模拟蚂蚁觅食行为机制，使得无人机在面向复杂校园环境中能够探寻出最优配送路径，以实现配送效率提升与成本降低。基于VSCode的仿真实验结果显示，该算法能有效优化无人机配送路径，显著提升校园物流配送效率与安全性，为校园物流智能化发展开拓新思路，助力无人机高效服务校园。

Abstract: In recent years, the volume of campus express delivery business has risen sharply, and the problems of low efficiency and high cost in the traditional distribution mode have become increasingly prominent. On the basis of the ant colony algorithm, this paper proposes a quadruple optimization mechanism and carries out simulation experiments of UAV campus logistics distribution. By constructing a solution model of the ant colony algorithm to simulate the foraging behavior of ants, the UAV can find the optimal distribution path in the complex campus environment, so as to improve distribution efficiency and reduce costs. Simulation results based on VSCode show that the algorithm can effectively optimize the UAV distribution path, significantly improve the efficiency and safety of campus logistics distribution, open up new ideas for the intelligent development of campus logistics, and help UAVs serve the campus efficiently.