Cooperative planning for safe transportation routes and flight paths of UAV with multiple dispatching centers and soft time windows

A two-layer collaborative planning model is established for the problem of unmanned aerial vehicle (UAV) transportation routes and flight paths planning for logistics distribution. In the upper-level model, considering constraint factors such as customer time windows, UAV load, energy consumption, and path risk, the UAV’s departure dispatching center, access sequence, and customer arrival times were calculated to minimize UAV dispatching costs. In the lower-level model, considering multiple safety factors such as obstacles, radio interference, and UAV crash costs, the shortest feasible flight path between any dispatching center and customers was calculated. A two-stage deep reinforcement learning (DRL) algorithm, incorporating the A^* algorithm and a greedy strategy, was designed to solve the problem based on its characteristics. Finally, a case studywas presented where the optimal UAV transportation route and flight path planning scheme were calculated, and the impact of changes in key parameters on the scheduling results was analyzed. The validity and accuracy of this paper were verified by comparison with genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO) algorithms.