关键词: 关键词：航空运输, 任务分配, GA-PSO融合算法, 改进A*算法, 双层耦合模型

Abstract: Abstract: Aiming at the problems of multi-factor coupling constraints, frequent operation risks, low transportation efficiency, and high spatio-temporal costs faced by the task allocation of logistics drones in urban low-altitude complex environments, a collaborative dynamic task allocation method for urban low-altitude logistics is proposed. Firstly, a static and dynamic environment model of urban low-altitude airspace is constructed by using digital elevation models and the grid method. On this basis, a fine evaluation of third-party social risks such as personal safety, noise pollution, property damage, and privacy leakage caused by the flight of urban low-altitude logistics drones is conducted. Secondly, considering factors such as obstacle avoidance, the timeliness of logistics tasks, and flight performance, a two-layer coupled model for collaborative dynamic task allocation of low-altitude logistics is proposed. The upper layer model aims to minimize transportation costs and optimizes task allocation collaboratively, while the lower layer aims to minimize third-party social risks and dynamically plans flight paths. The two layers are coupled and nested. Finally, a genetic algorithm for global search and particle swarm optimization for local optimization are fused, and an improved A* algorithm is designed to obtain the best allocation scheme. The case analysis shows that the collaborative dynamic task allocation results of urban low-altitude logistics obtained by the proposed method have improvements in climb time, descent time, risk, flight distance, and total flight time. Compared with traditional methods, they are optimized by an average of 19.60%, 23.44%, 19.00%, 22.90%, and 22.64%, respectively. Meanwhile, the applicability of the method is verified through heterogeneous drone collaborative experiments and multi-task scale gradient experiments, which can provide strong technical support for the efficient and safe operation of urban low-altitude logistics.

Key words: Key words: Air transport, Task allocation, GA-PSO fusion algorithm, Improve the A* algorithm, Double-layer coupling model