水下仿生侧线传感器阵列优化设计及试验研究

doi:10.3901/JME.2025.08.032

机械工程学报 ›› 2025, Vol. 61 ›› Issue (8): 32-46.doi: 10.3901/JME.2025.08.032

• 仪器科学与技术 • 上一篇

扫码分享

水下仿生侧线传感器阵列优化设计及试验研究

单刘毫¹, 李怡昕¹, 姚远基¹, 付同强¹, 杨倩¹, 唐龙¹, 胡桥^1,2,3

1. 西安交通大学机械工程学院西安 710049;
2. 西安交通大学机械制造系统工程国家重点实验室西安 710049;
3. 西安交通大学陕西省智能机器人重点实验室西安 710049

收稿日期:2024-04-13 修回日期:2024-10-27 发布日期:2025-05-10
作者简介:单刘毫，男，1999年出生，博士研究生。主要研究方向为传感器阵列优化和水面目标尾流流场探测。E-mail：liuhaoshan@stu.xjtu.edu.cn;胡桥(通信作者)，男，1977年出生，博士，教授，博士研究生导师。主要研究方向为海洋智能感知与仿生机器人、水陆两栖机器人、智能医疗机器人。E-mail：hqxjtu@xjtu.edu.cn
基金资助:
国家自然科学基金“叶企孙”联合基金(U2441288)和国家自然科学基金(5237133)资助项目。

Optimization Design and Experimental Study of Underwater Artificial Lateral Line Sensor Arrays

SHAN Liuhao¹, LI Yixin¹, YAO Yuanji¹, FU Tongqiang¹, YANG Qian¹, TANG Long¹, HU Qiao^1,2,3

1. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049;
2. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049;
3. Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an 710049

Received:2024-04-13 Revised:2024-10-27 Published:2025-05-10

摘要/Abstract

摘要： 为了解决现有水下航行器传感器布局优化中理论缺乏、传感器阵列分布简单和探测目标物运动形式单一等问题，基于贝叶斯概率模型的顺序启发式传感器优化布置方法，优化设计了探测两种不同运动类型目标物位置的传感器阵列，结合相对熵与顺序启发式算法构建目标函数，利用相关性系数设计评估函数，共同构建了仿生侧线传感器阵列优化理论模型。通过流场仿真构建了多种运动类型目标物的流场仿真数据库，使用不同运动类型目标物流场仿真数据概率表征不同阵列探测目标物位置的准确度。通过设计水下仿生侧线优化阵列探测试验系统，对所设计的优化阵列进行了探测准确性的优劣评估。仿真分析结果表明，相较于传统均匀阵列和经验阵列，所设计的优化阵列对水平和竖直振动偶极子目标物位置的探测概率分别平均提高了50.78%与 29.69%；优化阵列验证试验表明，相较于均匀阵列与经验阵列，所设计的优化阵列对水平和竖直振动偶极子目标物位置的探测概率分别平均提高了53.37%与9.80%，有效提高了对多运动类型目标物探测的准确度。

关键词: 水下航行器, 仿生侧线, 水平/竖直振动目标探测阵列优化, 贝叶斯概率模型

Abstract: In order to solve the problems of missing optimization theory, simple distribution of sensor array and single mode of detecting object motion in existing sensor array placement optimization of underwater vehicle, a sensor array for detecting the position of two different moving objects was optimized based on the sequential heuristic sensor layout method of Bayesian probability model. Combining the relative entropy and sequence heuristic algorithm to construct the objective function, and using the correlation coefficient to design the evaluation function, the theoretical model of artificial lateral line sensor array optimization is constructed. The flow field simulation database of various moving objects is constructed by flow field simulation. The probability of the flow field simulation data of different moving objects is used to characterize the accuracy of different arrays in detecting the location of objects. The detection accuracy of the optimized array is evaluated by designing the experimental system of underwater artificial lateral line optimization array. The simulation results show that compared with the traditional uniform array and the empirical array, the optimized array has an average detection probability of 50.78% and 29.69% higher for the position of the horizontal and vertical vibrating dipole target, respectively. The experimental results show that compared with uniform array and empirical array, the optimized array has an average detection probability of 53.37% and 9.80% higher for the position of the horizontal and vertical vibrating dipole target, respectively, which effectively improves the detection accuracy of multi-moving objects.

Key words: underwater vehicle, artificial lateral line, horizontal/vertical vibration target detection array optimization, bayesian probability model

中图分类号:

TB17

单刘毫, 李怡昕, 姚远基, 付同强, 杨倩, 唐龙, 胡桥. 水下仿生侧线传感器阵列优化设计及试验研究[J]. 机械工程学报, 2025, 61(8): 32-46.

SHAN Liuhao, LI Yixin, YAO Yuanji, FU Tongqiang, YANG Qian, TANG Long, HU Qiao. Optimization Design and Experimental Study of Underwater Artificial Lateral Line Sensor Arrays[J]. Journal of Mechanical Engineering, 2025, 61(8): 32-46.

参考文献

[1] 李超，王晓杰，宋佳坤. 鱼类的侧线机械感受系统和仿生学等应用研究[J]. 科学通报，2017，62(22)：2509- 2519. LI Chao，WANG Xiaojie，SONG Jiakun. Structure and function of the mechanosensory lateral line system in fish and biomimetic[J]. Chinese Science Bulletin，2017，62(22)：2509-2519.
[2] JOHN C M，CINDY F B，ALEXANDER G C. The lateral line can mediate rheotaxis in fish[J]. Nature：International Weekly Journal of Science，1997，389：960-963.
[3] MEKDARA P J，SCHWALBE M A B，COUGHLIN L L，et al. The effects of lateral line ablation and regeneration in schooling giant danios[J]. The Journal of Experimental Biology，2018，221(8)：1-12.
[4] BLECKMANN H，ZELICK R. Lateral line system of fish[J]. Integrative Zoology，2009，4(1)：13-25.
[5] LIU Guijie，WANG Anyi，WANG Xinbao，et al. A review of artificial lateral line in sensor fabrication and bionic applications for robot fish[J]. Applied Bionics and Biomechanics，2016，2016(5)：1-15.
[6] JIAN Yonggang，GONG Zheng，YANG Zhen，et al. Underwater source localization using an artificial lateral line system with pressure and flow velocity sensor fusion[J]. IEEEASME Transactions on Mechatronics，2021，27(1)：245-255.
[7] XIE Ou，SUN Zhaoguang，SHEN Can. A study on flow field characteristics of a self-propelled robot fish approaching static obstacles based on Artificial Lateral Line[J]. Bioinspir Biomim，2023，18(3)：1-17.
[8] PETER Z，KAMRAN M. Dipole model of vorticity at the moving contact line[J]. International Journal of Multiphase Flow，2018，103：169-172.
[9] BOT D M，WOLF B J，VAN NETTEN S M. The quadrature method：A novel dipole localisation algorithm for artificial lateral lines compared to state of the art[J]. Sensors，2021，21(13)：4558-4639.
[10] ZHENG Xiande，ZHANG Yong，JI Mingjiang，et al. Underwater positioning based on an artificial lateral line and a generalized regression neural network[J]. Journal of Bionic Engineering，2018，15(5)：883-893.
[11] 张勇，郑贤德，季明江，等. 基于鱼侧线感知原理和深度学习的水下平动目标方向识别[J]. 机械工程学报，2020，56(12)：231-239. ZHANG Yong，ZHENG Xiande，JI Mingjiang，et al. Underwater translational target direction recognition based on lateral line perception principle and deep learning[J]. Journal of Mechanical Engineering，2020，56(12)：231-239.
[12] TANG Zhijie，FENG Hao，LEI Jingtao，et al. Design and simulation of artificial fish lateral line[J]. International Journal of Advanced Robotic Systems，2019，16(1)：1-6.
[13] TANG Zhijie，WANG Zhen，LU Jiaqi，et al. Underwater robot detection system based on fish’s lateral line[J]. Electronics，2019，8(5)：566-577.
[14] AHRARI A，LEI Hong，SHARIF M A，et al. Design optimization of an artificial lateral line system incorporating flow and sensor uncertainties[J]. Engineering Optimization，2017，49(2)：328-344.
[15] AHRARI A，LEI Hong，SHARIF M A，et al. Reliable underwater dipole source characterization in 3D space by an optimally designed artificial lateral line system[J]. Bioinspiration & Biomimetics，2017，12(3)：036010- 036023.
[16] LIU Guijie，WANG Mengeng，WANG Anyi，et al. Research on flow field perception based on artificial lateral line sensor system[J]. Sensors，2018，18(3)：838-861.
[17] JI Mingjiang，ZHANG Yong，ZHENG Xiande，et al. Performance evaluation and analysis for dipole source localization with lateral line sensor arrays[J]. Measurement Science and Technology，2019，30(11)：1-14.
[18] WEBER P，ARAMPATZIS G，NOVATI G，et al. Optimal flow sensing for schooling swimmers[J]. Biomimetics，2020，5(1)：10-31.
[19] XU Dong，ZHANG Yuanlin，TIAN Jian，et al. Optimal sensor placement of the artificial lateral line for flow parametric identification[J]. Sensors，2021，21(12)：3980-3996.
[20] 李怡昕，胡桥，刘钰，等. 水下航行器仿生侧线探测阵列优化布置模型及评估方法[J]. 西安交通大学学报，2021，55(11)：12-25. LI Yixin，HU Qiao，LIU Yu，et al. Optimal placement model and evaluation scheme of artificial lateral line detection array for underwater vehicle[J]. Journal of Xi’an Jiaotong University，2021，55(11)：12-25.
[21] YANG Zhen，GONG Zheng，JIANG Yonggang，et al. Maximized hydrodynamic stimulation strategy for placement of differential pressure and velocity sensors in artificial lateral line systems[J]. IEEE Robotics and Automation Letters，2022，7(2)：2170-2177.
[22] WANG Mengmeng，JIN Bei，LIU Guijie，et al. The moving vibration source perception using bionic lateral line system and data-driven method[J]. Ocean Engineering，2022，247：110463.
[23] 王晓燕，李美洲. 浅谈等级相关系数与斯皮尔曼等级相关系数[J]. 广东轻工职业技术学院学报，2006，5(4)：26-27. WANG Xiaoyan，LI Meizhou. The relationship of rank correlation coefficient and spearman rank correlation coefficient[J]. Journal of Guangdong Industry Polytechnic，2006，5(4)：26-27.

水下仿生侧线传感器阵列优化设计及试验研究

Optimization Design and Experimental Study of Underwater Artificial Lateral Line Sensor Arrays

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	张涛, 李德骏, 彭时林, 夏庆超, 杨灿军. 基于视觉导引与旋转接驳的自主水下航行器末端入坞研究[J]. 机械工程学报, 2018, 54(20): 81-88.
[2]	吴乃龙, 吴超, 葛彤, 王涛, 张亚. 基于鱼类体线感知机理的水下机器人水流场识别研究^*[J]. 机械工程学报, 2016, 52(13): 54-59.
[3]	丁文俊, 宋保维, 毛昭勇, 赵晓哲. 浅水域探测型无人水下航行器波浪能发电系统设计[J]. 机械工程学报, 2015, 51(2): 141-147.
[4]	姜欣, 刘玉红, 朱光, 吴芝亮, 赵黎明. 深水自主水下航行器附体安装结构参数优化及涡流噪声分析[J]. 机械工程学报, 2015, 51(17): 25-34.
[5]	严新平;白秀琴;袁成清. 试论海洋摩擦学的内涵、研究范畴及其研究进展[J]. , 2013, 49(19): 95-103.
[6]	徐勤超;王树宗;练永庆. 水下航行器新型凸轮发动机点火起动仿真[J]. , 2012, 48(20): 168-173.
[7]	高富东;潘存云;杨政;冯庆涛. 多矢量推进水下航行器6自由度非线性建模与分析[J]. , 2011, 47(5): 93-100.