基于侧线感知机理对水下三角形干扰源感知方法的研究

doi:10.3901/JME.2020.12.240

机械工程学报 ›› 2020, Vol. 56 ›› Issue (12): 240-248.doi: 10.3901/JME.2020.12.240

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基于侧线感知机理对水下三角形干扰源感知方法的研究

林兴华¹, 武建国¹, 王晓鸣², 张敏革², 刘海涛¹

1. 河北工业大学机械工程学院天津 300130;
2. 天津科技大学机械工程学院天津 300222

收稿日期:2019-10-12 修回日期:2020-04-08 出版日期:2020-06-20 发布日期:2020-07-14
通讯作者: 武建国(通信作者),男,1980年出生,博士,研究员。主要研究方向为水下机器人设计和环境自适应性方法。E-mail:wujianguo@hebut.edu.cn
作者简介:林兴华,男,1990年出生,博士研究生。主要研究方向为水下机器人、计算流体力学和机器学习方法。E-mail:sslxh2009@126.com;王晓鸣,男,1981年出生,博士,副教授。主要研究方向为水下机器人设计与控制算法。E-mail:wxm@tust.edu.cn;张敏革,女,1980年出生,博士,副教授。主要研究方向为计算流体力学。E-mail:zhangminge@tust.edu.cn;刘海涛,男,1982年出生,博士,副教授,博士研究生导师。主要研究方向为材料力学分析与建模。E-mail:htliu@hebut.edu.cn
基金资助:
河北省自然科学基金（E2018202259）和河北省研究生创新（CXZZBS2017025）资助项目。

Research on Underwater Triangle Source Sensing Method Based on the Lateral Line Sensing Mechanism

LIN Xinghua¹, WU Jianguo¹, WANG Xiaoming², ZHANG Minge², LIU Haitao¹

1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130;
2. College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222

Received:2019-10-12 Revised:2020-04-08 Online:2020-06-20 Published:2020-07-14

摘要/Abstract

摘要： 为提高自主水下机器人（Autonomous underwater vehicle，AUV）的环境自适应能力，基于侧线感知机理，对水下目标进行形态识别和位置定位方法的研究。采用数值模拟的方法对流场中等边三角形的流场结构进行研究，提取"侧线"上的压力信号作为形态识别信息，训练并建立支持向量机（Support vector machine，SVM）识别模型。采用二步网格寻优的方法对SVM模型中的惩罚因子C和核函数参数g进行优化，模型测试表明，基于压力系数的时域波形结构可以对干扰源的形态进行辨识。通过提取压力系数波形中的特征值，对相对检测距离进行分析和拟合，结果表明利用压力信号的振幅，可以有效地计算出干扰源的相对位置。因此证明利用压力信号和SVM的方法，可以对水下目标进行识别和定位，为提高AUV的环境自适应能力提供了一种新思路。

关键词: 侧线系统, 支持向量机, 流场感知, 数值模拟, 目标识别

Abstract: In order to improve the environment adaptive ability of autonomous underwater vehicle (AUV), a method of form recognition and position location of underwater target is studied based on the lateral line sensing mechanism. The flow field structure of equilateral triangle is studied by numerical simulation. The pressure signal on the "lateral line" is extracted as identification information. And a support vector machine (SVM) recognition model is trained and established based on the data. The penalty factor and a kernel function parameter in SVM model is determined by two-step network method. The model test shows that the form of targets can be identified based on the pressure coefficient. The relative detection distance is analyzed and fitted by extracting the characteristic values of pressure coefficient waveform. The results show that the relative position of target can be calculated effectively based on the pressure amplitude. Therefore, it is proved that the pressure signal and SVM can be used to identify and locate underwater targets. The method provides a new idea for improving AUV environment adaptive ability.

Key words: lateral line system, support vector machine, flow sensing, numerical simulation, target recognition

中图分类号:

TG156

林兴华, 武建国, 王晓鸣, 张敏革, 刘海涛. 基于侧线感知机理对水下三角形干扰源感知方法的研究[J]. 机械工程学报, 2020, 56(12): 240-248.

LIN Xinghua, WU Jianguo, WANG Xiaoming, ZHANG Minge, LIU Haitao. Research on Underwater Triangle Source Sensing Method Based on the Lateral Line Sensing Mechanism[J]. Journal of Mechanical Engineering, 2020, 56(12): 240-248.

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基于侧线感知机理对水下三角形干扰源感知方法的研究

Research on Underwater Triangle Source Sensing Method Based on the Lateral Line Sensing Mechanism

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