Hierarchical Task Scheduling for Heterogeneous UAVs based on Hybrid Genetic Algorithm

The unmanned aerial vehicle (UAV) swarm is becoming an important part of the future battlefield. In highly adversarial battlefield environments, when the UAV swarm is damaged, the remaining UAVs cannot complete tasks due to the lack of payload resources. To address the problem, a hierarchical task-UAV scheduling model is proposed based on a heterogeneous UAV resource pool. Surviving UAVs can access the resource pool; once the payloads of UAVs are sufficient, reconstruction of the UAV swarm will be undertaken for the specific target tasks. In addition, potential channel usage conflicts between UAV swarms are considered, and a mechanism is introduced to adjust channel usage time. To obtain the task assignment scheme, a hybrid genetic algorithm with the Boltzmann survival mechanism and a tabu search method is proposed. The simulation results show that the proposed algorithm outperforms other algorithms in task assignment scenarios.