装卸一体的无人机配送线路和航迹双层协同规划模型  

A Bi-level Cooperative Planning Model of UAV Distribution Routes and Trajectories with Integrated Loading and Unloading

作　　者：孙博 魏明 孙雅茹 王清琦 马文博 SUN Bo;WEI Ming;SUN Yaru;WANG Qingqi;MA Wenbo(School of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China;Key Laboratory of General Aviation Technology of Henan Province,Zhengzhou Institute of Aviation Industry Management,Zhengzhou 450015,China;Institute of Navigation New Technology,Academy of Civil Aviation Science and Technology of China,Beijing 100100,China)

机构地区：[1]中国民航大学空中交通管理学院,天津300300 [2]郑州航空工业管理学院河南省通用航空技术重点实验室,河南郑州450015 [3]中国民航科学技术研究院航行新技术研究所,北京100100

出　　处：《工业工程》2025年第1期34-39,共6页Industrial Engineering Journal

基　　金：教育部人文社会科学基金资助项目(20YJCZH176);民航通用航空运行重点实验室开放基金资助项目(CAMICKFJJ-2023-01);河南省通用航空技术重点实验室开放基金资助项目(ZHKF-240210);中国民航科学技术研究院基本科研业务费专项资金资助项目(X242060302235)。

摘　　要：无人机航迹是无人机配送线路的基础,且两者相互影响,其协同规划问题引起国内外学者的广泛关注。在GIS栅格化基础上,考虑客户需求的多样性,根据调度中心、客户、障碍物和高空坠落代价的单元格位置空间分布,建立装卸一体的无人机配送线路和航迹双层协同规划模型,寻求无人机航迹和机队调度成本之间的最佳耦合关系。根据问题特征,设计求解该问题的嵌入A*算法的Q-Learning两阶段算法,在第1阶段利用Q-Learning完成订单分配的基础上构建无人机配送线路,在第2段将A*算法嵌入寻找无人机访问任意两点之间航迹,通过两阶段算法交互分别求解双层规划模型的解。最后,结合真实案例,计算最优调度方案,分析模型参数的灵敏度,并对比启发式算法和本算法的求解性能,从而验证模型和算法的有效性。UAV trajectories are the foundation of UAV distribution route planning,and the two are interdependent.A bilevel cooperative planning model of UAV distribution routes and trajectories with integrated loading and unloading is established based on GIS rasterization and considering the diversity of customer demand.The model aims to find the optimal coupling relationship between UAV trajectories and fleet scheduling cost considering the spatial distribution of scheduling centers,customers,obstacles and high fall-cost cells.According to the characteristics of the problem,a bi-level Q-Learning algorithm embedded with A*algorithm is designed to solve the problem.At the first level,Q-Learning is used to complete the order distribution and generate UAV distribution routes.At the second level,A*algorithm is embedded to find trajectories between any two points visited by UAVs.The solution of the bi-level model is obtained by the proposed algorithm.Finally,a real case is used to verify the effectiveness of the model and algorithm,by calculating the optimal scheduling plan,analyzing parameter sensitivity,and comparing the solving performance between heuristic algorithms and the proposed algorithm.

关 键 词：无人机配送线路 航迹规划 装卸一体 Q-Learning算法 A*算法 

分 类 号：U8[交通运输工程] F403[经济管理—产业经济]