Load-Balanced Pickup Strategy for Multi-UAV Systems with Heterogeneous Capabilities

This paper investigates a load-balanced pickup strategy for heterogeneous multi-UAV systems, where unmanned aerial vehicles (UAVs) with different flight speeds and payload capacities cooperatively collect spatially distributed parcels while avoiding no-fly zones. The goal is to minimize the maximum mission completion time among UAVs while ensuring balanced workload distribution according to their heterogeneous capabilities. The formulated problem is a mixed-integer nonlinear program that jointly optimizes pickup assignment, trajectory planning, and slot duration allocation under mobility, safety, and payload constraints. To address the nonconvexity of the optimization problem, the successive convex approximation and penalty convex–concave procedure are applied, leading to a two-stage iterative algorithm that efficiently derives practical UAV strategies for load-balanced parcel pickup. The first stage minimizes the maximum completion time, and the second stage further refines the trajectories to reduce the total travel distance. Simulation results demonstrate that the proposed scheme effectively adapts to UAV capability asymmetry and achieves superior time efficiency compared to benchmark schemes. The results also point to future research opportunities, such as incorporating energy models, communication constraints, or stochastic task dynamics to extend the applicability of the proposed framework.