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Study on Multi-UAV Task Clustering and Task Planning in Cooperative Reconnaissance

机译:协同侦察中的多无人机任务聚类与任务计划研究

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For multi-UAV cooperative reconnaissance to enemy's multi-task points, because of multi-task, reasonable clustering is needed and the task clustering model should be established. In this paper, the task planning model is established according to task clustering of each UAV, and the sequence of task execution is determined. Reasonable task clustering optimization index is put forward. Task allocation is proposed based on improved K-means clustering algorithm of simulated annealing. The shortest path task planning is designed using the simulated annealing algorithm, which makes multi-UAV relatively balanced in the assignments, the task group in the group centralized distribution, inter-group distribution scattered and the total cruise time shortest. Simulation results show that the task clustering is well achieved and the optimum task planning program is obtained. The validity of the model and algorithm is verified and the algorithm has certain theoretical and practical value.
机译:对于多UAV协同侦察敌方的多任务点,由于存在多任务,因此需要合理的聚类,并应建立任务聚类模型。本文根据每架无人机的任务聚类建立任务计划模型,并确定任务执行的顺序。提出了合理的任务聚类优化指标。提出了一种基于改进的模拟退火K均值聚类算法的任务分配方法。最短路径任务计划是使用模拟退火算法设计的,它使多UAV在分配,组集中分布的任务组,组间分布分散的任务组和总巡航时间最短的情况下相对平衡。仿真结果表明,该算法很好地实现了任务聚类,并获得了最优的任务计划程序。验证了该模型和算法的有效性,该算法具有一定的理论和实用价值。

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