自主水下机器人(AUV)可以在深海海域进行资源勘査、目标搜寻等任务。在海上现场通常面临需要快速响应任务布署，进行AUV搜寻测绘任务的路径规划。本文在AUV多疑似目标点位搜寻任务中提出了一种先聚类后搜寻的AUV多目标搜寻方法，将大范围待测海域内的多个目标点进行K-means聚类,形成多个待测区域，减少对非目标区的搜寻，确保AUV携带有限能源下更高的搜寻效率；在多个待测区域内执行梳状路径方案，形成多区域内的AUV搜寻路径。开发了基于Tkinter的AUV多目标聚类搜寻软件，通过可视化交互工具实现本文所提方法，解决人工规划费时、易出错等问题，提高AUV布署效率。最后，对比不同聚类个数之间AUV路径的长短，确定本文方法的可行性及应用价值。

Autonomous underwater robots (AUVs) can perform tasks such as resource exploration and target search in deep sea areas. At offshore sites, we often face the need to quickly respond to mission deployment and conduct path planning for AUV search and mapping missions. This paper proposes an AUV multi-target search method that clusters first and then searches in the AUV multi-suspect target point search task. It performs K-means clustering on multiple target points in the large-scale sea area to be measured to form multiple target points to be tested. The measurement area reduces the search for non-target areas and ensures higher search efficiency when the AUV carries limited energy; the comb path scheme is implemented in multiple areas to be measured to form an AUV search path in multiple areas. An AUV multi-target clustering search software based on Tkinter was developed to implement the method proposed in this article through visual interactive tools, solving the problems of time-consuming and error-prone manual planning and improving the efficiency of AUV deployment. Finally, We compare the length of AUV paths between different cluster numbers to determine the feasibility and application value of this method.