基于时序耦合分析的无人机集群任务分配方法OA

With the increasing scale of UAV swarm and the growing complexity of mission scenarios,designing effi-cient task allocation algorithms has become a significant challenge for swarm applications.To address the allocation failures and conflicts caused by cross-queue influences in temporal task allocation for UAV swarm,a Temporal Cou-pling Analysis-based Task Allocation method(TCATA)is proposed.Firstly,a task allocation model considering UAV payloads,task requirements,and task temporal constraints is established.The impact of task temporal coupling con-straints under the market mechanism is analyzed from two aspects:performance function design and performance va-lidity.Next,a local adjustment set is constructed for each UAV within the swarm,and a global adjustment set is ob-tained through communication consensus.Then,based on the performance magnitude 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.Finally,the executors of the tasks are determined and the global task assignment result is updated.Simulation experiments demonstrate that in solving temporal task assignment problems with hundreds of UAVs and tasks considering communication delays,TCATA significantly outperforms distributed ge-netic algorithms in both efficiency and performance metrics.Compared with the CBBA-TCC and CNP algorithm,TCATA achieves marginally superior performance while reducing executing time and iteration number by more than 50％,validating its effectiveness in large-scale sequential task allocation.