均衡聚类市场拍卖机制的异构无人机集群任务规划方法

针对大规模异构无人机集群的全局任务规划问题,提出一种基于均衡聚类市场拍卖机制的任务规划方法。对无人机群协同合作完成任务的场景进行分析,综合任务聚类和无人机联盟的优势,建立了通用性较高的任务规划模型。考虑到对无人机群负载均衡的需求,融合和改进了K-means聚类算法和市场拍卖机制,形成一种综合考虑路程消耗和任务消耗的均衡聚类市场拍卖算法。在拍卖过程中引入平衡参数,通过计算旅行商问题来修正平衡参数,保证无人机群在负载均衡的同时整体成本不断降低。仿真结果表明,使用均衡聚类市场拍卖机制的任务规划方法能够在较短时间内完成异构无人机群的复杂任务规划,保证无人机群负载均衡的同时,整体成本和总时间上也有较好表现,具有一定的实际应用价值。

Task planning of heterogeneous UAV swarm based on balanced clustering market auction mechanism

Aiming at the global task planning problem of large-scale heterogeneous UAV (unmanned aerial vehicle) swarm, a task planning method based on balanced clustering market auction mechanism was proposed. Scene of completing tasks by collaborative UAVs was analyzed, and a task planning model with high generality was established by combining the advantages of task clustering and UAV coalition. Considering the demand for load balance of UAVs, a new balanced clustering market auction algorithm which comprehensively considers the travel consumption and task consumption was established by integrating and improving the K-means algorithm and market auction mechanism. The balance parameter was introduced into the auction process. By solving the traveling salesman problem to modify the balance parameter, the total cost was continuously reduced while ensuring the load balance. The simulation results show that the task planning method using balanced clustering market auction mechanism can complete the complex task planning of heterogeneous UAV swarm in a short time, ensures the load balance of UAV coalitions, and has good performance in total cost and total time, exhibiting certain practical application value.