Research on a UAV Task Allocation and Path Planning Model Based on Dynamic Payload

To capture the effects of payload reduction on energy estimation and route feasibility in multi-stop deliv ery, a joint UAV task allocation and path planning model is developed. The model considers a single-depot scenario and minimizes fixed UAV utilization, travel, energy, and time-window violation costs. The problem is formulated as a capacitated vehicle routing problem with time windows (CVRPTW), in which time-window violations incur additional costs. The remaining payload is updated for each flight segment according to the delivery-station visitation sequence. UAV operations are divided into vertical takeoff, horizontal flight, and vertical landing. A phase-based dynamic-payload energy model accounts for altitude-dependent air density and the cumulative effects of repeated takeoffs and landings. The resulting route energy cost is incorporated into the total economic cost. A two-stage solution method is designed. In the first stage, an improved A* algorithm searches for the shortest obstacle-avoiding paths between stations. Directional search and synchronous bidirectional expansion reduce redundant searches, while greedy pruning is applied to construct the distance matrix. In the second stage, an improved Clarke-Wright savings algorithm (C-WSA) merges the initial routes based on this matrix. The distance-based savings metric is extended to a composite economic savings metric comprising the four cost components. Simulation results show that the model generates seven feasible delivery routes. Takeoff and landing energy accounts for 22.53%—30.20% of the total energy consumption of each route. Removing the energy constraint reduces total travel distance by 10.92% but produces infeasible routes whose energy consumption exceeds the battery capacity. For the same delivery routes, total energy estimates from equivalent constant-payload and segment-wise dynamic-payload calculations differ by 16.38%. This difference indicates that dynamic-payload effects cannot be neglected in energy estimation. The improved A* algorithm reduces search time by 73.57% relative to the conventional A* algorithm. The improved C-WSA reduces total economic cost by 26.38%—36.64% relative to the benchmark algorithms. These results demonstrate the effectiveness of the proposed two-stage solution method.