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

Zhao Junwei, Zhao Jianjun
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引用次数: 23

Abstract

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.
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协同侦察中多无人机任务聚类与任务规划研究
多无人机协同侦察敌方多任务点,由于任务多,需要合理的聚类,建立任务聚类模型。本文根据每架无人机的任务聚类,建立任务规划模型,确定任务执行顺序。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。采用模拟退火算法设计最短路径任务规划,使多架无人机的任务分配相对均衡,任务群内集中分布,群间分散分布,总巡航时间最短。仿真结果表明,该方法能很好地实现任务聚类,得到最优的任务规划方案。验证了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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