北京航空航天大学学报2026，Vol.52Issue(1)：94-103,10.DOI:10.13700/j.bh.1001-5965.2023.0719
面向多无人机物流配送的双层任务规划方法
Two-layer task planning method for multi-UAV logistics distribution
摘要
Abstract
In urban UAV logistics distribution,the two main components that must be merged are multi-UAV task collaborative allocation and distribution path design.In order to ensure the safety and efficiency of multi-UAV logistics distribution,the grid method is used to model the ultra-low space environment of a three-dimensional city,and the grid risk calculation method is described.Secondly,a two-layer programming model of UAV distribution route and flight path collaborative planning is constructed.In the upper layer model,considering the constraints of UAV load and maximum range,a genetic algorithm is introduced to determine the UAV distribution order with the goal of minimum delay penalty cost.In the lower model,a comprehensive improved particle swarm optimization(CIPSO)algorithm is proposed to solve the flight path of the UAV by considering the performance constraints of the UAV and aiming at the minimization of timeliness cost,UAV height variation,and grid risk.Last but not least,the simulation results demonstrate that CIPSO's overall cost is lower than that of particle swarm optimization(PSO)and improved acceleration coefficients particle swarm optimization(ICPSO)algorithm by 65.00%and 38.41%,respectively.This suggests that the constructed model and the proposed algorithm developed in this study are both practical and efficient.关键词
物流无人机/任务分配/路径规划/双层规划模型/改进粒子群优化算法Key words
logistics unmanned aerial vehicle/task allocation/path planning/two-layer programming model/improved particle swarm optimization algorithm分类
航空航天引用本文复制引用
王飞,杨清平..面向多无人机物流配送的双层任务规划方法[J].北京航空航天大学学报,2026,52(1):94-103,10.基金项目
天津市应用基础多元投入基金重点项目(21JCZDJC00840) （21JCZDJC00840）
中央高校基本科研业务费专项资金(3122019129) Tianjin Application Basic Diversified Investment Fund Key Project(21JCZDJC00840) （3122019129）
The Fundamental Research Funds for the Central Universities(3122019129) （3122019129）
