Evergrande School of Management, Wuhan University of Science and Technology, Wuhan 430065, China.
Sensors (Basel). 2022 Mar 5;22(5):2045. doi: 10.3390/s22052045.
Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the issue of vehicle-assisted UAV delivery, we did not find any studies on the scenario of an UAV serving several customers. This study aims to design a new vehicle-assisted UAV delivery solution that allows UAVs to serve multiple customers in a single take-off and takes energy consumption into account. A multi-UAV task allocation model and a vehicle path planning model were established to determine the task allocation of the UAVs as well as the path of UAVs and the vehicle, respectively. The model also considered the impact of changing the payload of the UAV on energy consumption, bringing the results closer to reality. Finally, a hybrid heuristic algorithm based on an improved -means algorithm and ant colony optimization (ACO) was proposed to solve the problem, and the effectiveness of the scheme was proven by multi-scale experimental instances and comparative experiments.
无人机（UAV）在即时配送场景中越来越多地被使用。与各自独立的配送相比，车辆和无人机的联合配送具有许多优势，能够极大地提高配送效率。尽管文献中有一些研究探讨了车辆辅助无人机配送的问题，但我们没有发现任何关于无人机为多个客户服务的场景的研究。本研究旨在设计一种新的车辆辅助无人机配送解决方案，该方案允许无人机在单次起飞时为多个客户服务，并考虑能耗。建立了多无人机任务分配模型和无人机路径规划模型，分别确定无人机的任务分配以及无人机和车辆的路径。该模型还考虑了改变无人机有效负载对能耗的影响，使结果更接近实际情况。最后，提出了一种基于改进均值算法和蚁群优化（ACO）的混合启发式算法来解决该问题，并通过多尺度实验实例和对比实验验证了该方案的有效性。
