RESEARCH ON BILEVEL TASK PLANNING METHOD FOR MULTI-UAV LOGISTICS DISTRIBUTION

Multi-unmanned aerial vehicle (UAV) collaborative task planning and distribution path planning are the core content of agricultural UAV logistics distribution. In this study, the multi-UAV collaborative task planning and the distribution path planning were discussed, and such constraint conditions as UAV load capacity, battery capacity and flight time were comprehensively considered, aiming to reduce the number of UAVs and their power consumption. To ensure the safe and efficient completion of multi-UAV logistics distribution tasks, 3D agricultural ultralow space was subjected to environment modeling, and a bilevel planning model for collaborative planning of UAV distribution route and flight path was constructed. Then, an improved particle swarm optimization (PSO) algorithm with the improved learning factor and inertia coefficient was designed on the basis of PSO framework, and the global optimal solution in the current iteration was improved using variable neighborhood descent search. The feasibility of the proposed algorithm was verified by analyzing a practical case. With the central city area of XX City as the study area, 1 logistics & freight transportation center was taken as the central warehouse (coordinates: 50, 50, unit: km) and 50 intelligent express cabinets as the express cabinets of UAVs. The obtained results were comparatively analyzed with those acquired through the basic PSO algorithm. The results manifest that the proposed algorithm performs better than the compared algorithms. The improved PSO algorithm is superior to the basic PSO algorithm in aspects of total UAV flight distance, number of UAVs used and algorithm convergence time, indicating that the model and algorithm established in this study are feasible and effective.