摘要
【目的】针对单一无人机(unmanned aerial vehicle,UAV)配送中存在的续航短、载重小及地面交通制约等问题,为突破传统“无人机+车辆”协同模式的局限,提出一种全空域异构无人机群两级协同物流配送架构,旨在通过层级化资源调度优化来提升配送效率。【方法】构建以总飞行距离最小为目标的两级协同调度模型:大型运输无人机(large transport unmanned aerial vehicle,l-UAV)负责长距离转运以及发射/回收小型配送无人机(express unmanned aerial vehicle,e-UAV),e-UAV执行末端配送。设计三阶段混合启发式算法:动态质量约束聚类算法K-means++划分客户点集群并确定发射点位置;改进蚁群算法(ant colony optimization algorithm,ACO)优化l-UAV路径;禁忌搜索算法(tabu search algorithm, TS)规划e-UAV配送路径。【结果】基于Solomon数据集的多组算例表明:“ACO+TS”组合算法在飞行距离和飞行时间优化上显著领先;灵敏度分析揭示e-UAV在载重为8 kg时的系统性能最优,同时也验证了模型的鲁棒性。【结论】“l-UAV+e-UAV”两级协同模式通过全空域链路规避地面交通瓶颈、动态回收机制与组合优化算法,能显著缩短配送时间,为低空物流网络提供了高效的调度方案。
[Purposes]To address the issues of short flight endurance,low payload,and ground traffic limitations in single unmanned aerial vehicle(UAV),a two-level collaborative logistics distribution structure using heterogeneous UAV swarms across the entire airspace was proposed.This aims to enhance delivery efficiency through hierarchical resource scheduling,thus breaking the limitation in the traditional“UAV+vehicle”collaboration mode.[Methods]A two-level collaborative scheduling model was established with the goal of minimizing the total flight distance.Large transport unmanned aerial vehicles(l-UAVs)were responsible for long-distance transfers and the launch/retrieval of express unmanned aerial vehicles(e-UAVs),while e-UAVs handled the final delivery.A three-phase hybrid heuristic algorithm was designed:a dynamic weight constraint K-means++clustering algorithm was employed to divide customer points into clusters and determine launch points;an improved ant colony optimization algorithm(ACO)was used to optimize the paths of l-UAVs,and a tabu search algorithm(TS)was adopted to plan the delivery paths of e-UAVs.[Findings]Solomon dataset-based experiments show that the ACO+CS combination algorithm takes the lead in flight distance and time optimization.Sensitivity analysis indicates that the system performs best when e-UAVs have a payload of 8 kg,proving the model’s robustness.[Conclusion]The proposed“l-UAV+e-UAV”two-level collaboration mode avoids ground traffic bottlenecks via the entire airspace link.Its dynamic recovery mechanism and combined optimization algorithms significantly shorten delivery time,offering an efficient scheduling scheme for low-altitude logistics networks.
作者
柳伍生
余玉竹
LIU Wusheng;YU Yuzhu(School of Transportation,Changsha University of Science&Technology,Changsha 410114,China)
出处
《长沙理工大学学报(自然科学版)》
2025年第4期78-92,共15页
Journal of Changsha University of Science & Technology:Natural Science
基金
国家自然科学基金项目(61773077)
教育部人文社科规划基金项目(23YJAZH089)。
关键词
异构无人机群
两级协同配送
协同调度优化
蚁群算法
禁忌搜索算法
heterogeneous unmanned aerial vehicle swarm
two-level collaborative distribution
collaborative scheduling optimization
ant colony optimization
tabu search
