多 UUV搜索海底声信标任务规划方法 ………………………

张宏瀚*，郭焱阳，许亚杰，李本银，严浙平哈尔滨工程大学自动化学院，黑龙江哈尔滨 150001

摘 要:［目的］为了提高特定海域内多水下无人航行器（ UUV）执行海底声信标搜索任务时的搜索性能，需增加对目标的搜索概率。［方法］首先，给出各UUV所载被动声呐的搜索能力指标函数，采用蒙特卡罗方法模拟海底声信标的坐标位置，并在任务区域建立搜索能力函数，从而得到本次优化任务的优化目标；然后，根据 UUV实际执行任务时的队形要求建立本次优化的约束条件，整合得到基于海底声信标搜索概率最大化的多 UUV队形优化模型，并使多UUV按照此队形完成指定区域的声信标搜索工作；最后，采用遗传算法对优化模型进行参数优化，通过设定合理的目标函数以及改进传统的遗传算子使目标函数的值达到设定标准，随之取出相应的参数完成值的选择。［结果］将求解出的优化队形与传统优化队形进行对比发现，求解出的优化队形具有更高的发现海底信标的平均概率。［结论］该方法能够有效提升多UUV对海底声信标的搜索性能，并给出合理的队形优化方案。关键词：多水下无人航行器；队形优化；搜索概率；遗传算法中图分类号: U676.8+3 文献标志码:A DOI：10.19693/j.issn.1673-3185.01641

Mission planning method of multi-UUV search submarine acoustic beacon ZHANG Honghan*, GUO Yanyang, XU Yajie, LI Benyin, YAN Zheping College of Automation, Harbin Engineering University, Harbin 150001, China

Abstract: ［Objectives ］ This study is focused on improving the performance and increasing the detection probability of a multiple Unmanned Underwater Vehicle （UUV） seabed acoustic beacon searching a specific sea area.［Methods］First, the search ability index function of the passive sonar is given. Second, the Monte Carlo method is used to randomly simulate the coordinate position of the submarine acoustic beacon. Third, the cluster search capability function is established and the optimization goal of the optimization task is obtained. The optimization constraints are established in combination with the formation requirements of the UUV actually performing the task. Finally, the integration is based on the seabed acoustic beacon search probability. The cluster formation optimization model is maximized and the UUV cluster is made to complete the acoustic beacon search work in the specified area according to this formation. In this paper, the genetic algorithm is used to optimize the parameters of the optimization model. By setting a reasonable objective function and improving the traditional genetic operator, the value of the objective function reaches the set standard. At this point, the corresponding parameters are taken out to complete the value selection.［Results］By comparing this new optimized formation with the traditional optimized formation, it is found that the optimized formation has a higher average probability of finding the submarine beacon.［Conclusions ］ The optimized model test results show that the proposed method can effectively improve the submarine acoustic beacon search performance of UUV clusters and provide a reasonable formation optimization scheme. Key words: multiple unmanned underwater vehicle；formation optimization；detection probability；genetic algorithm