Multi-UAV Network Logistics Task Allocation Algorithm Based on Mean-Field-Type Game

Unmanned aerial vehicles (UAVs) has become the vital driving force of logistics distribution development as an important carrier of advanced productivity. However, it is challenging to efficiently allocate logistics tasks on a large scale with the lowest energy consumption, considering the selfishness of UAVs. To tackle the above problem, we propose a mean field type game (MFTG) based logistics task allocation scheme in the multi-UAV networks. Specifically, we first develop a MFTG framework to fully model the interactions between the UAVs, the influence of aerodynamics and the features of tasks. Then, we propose the consensus-based bundle algorithm (CBBA) to provide a feasible and conflict-free solution to the multi-UAV network task allocation problem under multiple interactions in the dynamic environment. Extensive simulations are finally conducted, and results demonstrate that the proposed scheme can efficiently reduce the energy consumption of the multi-UAV network and provide users with high-quality task transportation service.