“低空经济”作为一种新型的综合性经济形态，已经渗透进巡检、物流、救援、通信等多个领域。本文主要研究无人机在卡车配送点起降的新型配送模式，考虑无人机在卡车路径上的任意位置动态起降以减少无人机的飞行距离和等待时间，并在配送系统中引入多架无人机以提高系统配送效率。针对考虑动态同步的单卡车多无人机协同配送路径优化问题，以最小化加权总成本为目标建立路径优化模型；根据模型设计自适应大邻域搜索算法求解，明确算法初始解构造方法和各算子功能，使用模拟退火算法的Metropolis准则避免算法陷入局部最优；采用不同规模算例验证算法的可行性，并将无人机在卡车配送点起降结果与无人机在卡车路径上动态起降结果对比，对模型中各成本组成的权重关系和配送系统中的无人机数量变化进行分析。结果表明，无人机在卡车路径上动态起降策略的配送系统对于加权总成本的优化程度为0.80%~2.96%，且动态同步机制对等待成本变化敏感程度较高。随着等待成本权重增加，优化后的配送系统加权总成本逐渐降低，同时，增加等待成本可在一定程度上提升动态同步优化程度。当系统中存在2架无人机时，动态同步优化程度最高。本文所提出的卡车-无人机动态同步方案可优化卡车和无人机的配送路径，为降低城市末端物流配送成本提供新的思路。

The “low-altitude economy” is a new and comprehensive economic model that has permeated multiple fields such as inspection, logistics, rescue, and communication. This paper mainly investigates a new delivery mode involving drones taking off and landing at truck delivery points, considering dynamic takeoff and landing at any point along the truck’s path to reduce the drone’s flight distance and waiting time. Additionally, multiple drones are introduced into the delivery system to enhance delivery efficiency. This paper focuses on a collaborative delivery path optimization problem with one truck and multiple drones, incorporating dynamic synchronization. The goal is to minimize the weighted total cost, and a path optimization model is developed for this purpose. An adaptive large neighborhood search algorithm is designed to solve the model, with a clear construction method for the algorithm’s initial solution and the functionality of each operator. The Metropolis criterion of the simulated annealing algorithm is used to avoid the algorithm from falling into local optima. The feasibility of the algorithm is verified through examples of various scales, and the results of drones taking off and landing at truck delivery points are compared with those of drones dynamically taking off and landing along the truck’s path. The weight relationships of various costs in the model and the number of drones in the delivery system are also analyzed. The results show that the dynamic takeoff and landing strategy for drones along the truck path optimizes the weighted total cost by 0.80% to 2.96%, and the dynamic synchronization mechanism is highly sensitive to changes in waiting costs. As the weight of waiting costs increases, the optimized delivery system’s weighted total cost gradually decreases. Meanwhile, increasing the waiting cost can, to some extent, improve the dynamic synchronization optimization degree. The highest degree of dynamic synchronization optimization was achieved when the system operated with two drones. The truck-drone dynamic synchronization scheme proposed in this paper can optimize the delivery paths for both trucks and drones, offering new insights into reducing the cost of urban last-mile logistics delivery.