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Cooperative search method for multiple AUVs based on target clustering and path optimization

机译:基于目标聚类和路径优化的多个AUV的协同搜索方法

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摘要

A search method for uncertain targets using multiple autonomous underwater vehicles (AUVs) is studied. To improve search efficiency, a cooperative search method based on target clustering and path optimization is proposed to reduce reactive consumption and obtain more revenue. Firstly, all the target grids are connected and the target areas are clustered according to the theory of minimum spanning tree. Secondly, the moving paths of AUVs are studied and divided into two stages: transferring to a target area and detecting in a target grid. And both of the stages are then optimized. Finally, a series of experiments in different cases are carried out, and the cooperative search method through target clustering and path optimization is compared with another two algorithms. Results show that the cooperative search method proposed in this paper can obtain a larger amount of total revenue in the same period of time with more stable performance than the other algorithms. It is proved that this method conduces to a more thorough search of the target areas.
机译:研究了使用多个自主水下车辆(AUV)的不确定目标的搜索方法。为了提高搜索效率,提出了一种基于目标聚类和路径优化的协同搜索方法,以降低功率消耗并获得更多收入。首先,所有目标网格都连接,目标区域根据最小生成树的理论聚集。其次,研究了AUV的移动路径并分成了两个阶段:将到目标区域传送并检测目标网格。然后两级阶段优化。最后,执行不同情况下的一系列实验,并将通过目标聚类和路径优化的协作搜索方法与另外两个算法进行比较。结果表明,本文提出的合作搜索方法可以在同一时间段内获得更大的总收入,而不是比其他算法更稳定。事实证明,该方法涉及更彻底地搜索目标区域。

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