Air Route Network Planning Method of Urban Low-Altitude Logistics UAV with Double-Layer Structure

• Published in: Drones (Basel) Vol. 9; no. 3; p. 193
• Main Authors: Li, Zhuolun, Li, Shan, Lu, Jian, Wang, Sixi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Air traffic control; Air traffic management; Algorithms; Altitude; Aviation; Customer services; double-layer algorithm; Drone aircraft; Drones; Efficiency; Flight; Genetic algorithms; Graph theory; Infrastructure; Logistics; logistics UAV; Low altitude; Mathematical optimization; Methods; Nodes; route network design; Simulated annealing; Sorting algorithms; Traffic congestion; Traffic intersections; Unmanned aerial vehicles; urban air delivery; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones9030193

With the rapid development of e-commerce, logistics UAVs (unmanned aerial vehicles) have shown great potential in the field of urban logistics. However, the large-scale operation of logistics UAVs has brought challenges to air traffic management, and the competitiveness of UAV logistics is still weak compared with traditional ground logistics. Therefore, this paper constructs a double-layer route network structure that separates logistics transshipment from terminal delivery. In the delivery layer, a door-to-door distribution mode is adopted, and the transshipment node service location model is constructed, so as to obtain the location of the transshipment node and the service relationship. In the transshipment layer, the index of the route betweenness standard deviation (BSD) is introduced to construct the route network planning model. In order to solve the above model, a double-layer algorithm was designed. In the upper layer, the multi-objective simulated annealing algorithm (MOSA) is used to solve the transshipment node service location issue, and in the lower layer, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the network planning model. Based on real obstacle data and the demand situation, the double-layer network was constructed through simulation experiments. To verify the network rationality, actual flights were carried out on some routes, and it was found that the gap between the UAV’s autonomous flight route time and the theoretical calculations was relatively small. The simulation results show that compared with the single-layer network, the total distance with the double-layer network was reduced by 62.5% and the structural intersection was reduced by 96.9%. Compared with the minimum spanning tree (MST) algorithm, the total task flight distance obtained with the NSGA-II was reduced by 42.4%. The BSD factors can mitigate potential congestion risks. The route network proposed in this paper can effectively reduce the number of intersections and make the UAV passing volume more balanced.