“最后一公里”物流配送是物流终端面对客户的最后一环。车载无人机的配送模式克服了道路对车辆的限制，但由于无人机的电池容量有限导致续航时间短，而每次完成配送任务回收无人机需要更换电池，使得人力成本以及配送时间的增加。基于这种无人机车辆的协同配送模式，考虑了无人机的电池电量消耗和在车辆上的充电速率限制，提出无人机在无配送任务时在车辆进行充电的配送路径规划模型。设计了一种结合遗传算法和邻域搜索算法的改进GRASP（greedy randomized adaptive search procedure）算法来求解模型。基于设计测试集进行测试实验，并对影响路径规划的主要因素进行灵敏度分析，以充分利用无人机的配送能力。

The "last kilometer" logistics distribution is the last link of the logistics terminal facing the customers. The road limitations of the vehicle are overcome through the vehicle-mounted drone distribution mode, but due to the short endurance time because of the limited capacity of the battery of the drone, each battery of the drone need to be replaced after delivery mission is completed, which makes the labor cost as well as the increase of the distribution time. Based on this cooperative distribution mode of unmanned aerial vehicle and vehicle, the battery power consumption of UAV (unmanned aerial vehicle) and charging rate limitation on vehicle were considered, a distribution path planning model for charging on vehicle when UAV had no delivery task was established. An improved GRASP(greedy randomized adaptive search procedure) algorithm combining genetic algorithm and neighborhood search algorithm was designed to solve this model. Test experiments are conducted based on the designed test set, and sensitivity analysis is conducted on the main factors affecting the path planning to make full use of the UAV's distribution capability.