关键词: 无人机集群, 时序任务耦合约束, 分布式任务分配, 最大加权团, 市场机制

Abstract: To address the allocation failures and conflicts caused by cross-queue influences in temporal task allocation for UAV swarm, a Temporal Coupling Analysis-based Task Allocation method (TCATA) is proposed. Firstly, a task allo-cation model considering UAV payloads, task requirements, and task temporal constraints is established. The im-pact of task temporal coupling constraints under the market mechanism is analyzed from two aspects: performance function design and performance validity. Next, a local adjustment set is constructed for each UAV within the swarm, and a global adjustment set is obtained through communication consensus. Then, based on the performance mag-nitude and conflict relationships of each adjustment, the global adjustment scheme decision problem is modeled as a maximum weighted clique problem and solved by a greedy algorithm by each UAV individually. Finally, the execu-tors of the tasks are determined and the global task assignment result is updated. Simulation experiments demon-strate that in solving temporal task assignment problems with hundreds of UAVs and tasks, TCATA significantly out-performs distributed genetic algorithms in both efficiency and performance metrics. Compared with the CBBA-TCC algorithm, TCATA achieves comparable performance while reducing computation time by more than 50%, vali-dating its effectiveness in large-scale sequential task allocation.

Key words: Unmanned aerial vehicles swarm, Distributed task allocation, Temporal task coupling constraint, Maximum weighted clique, Market mechanism