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水下仿生侧线感知研究进展

翟宇凡 熊明磊 王晨 谢广明

翟宇凡, 熊明磊, 王晨, 等. 水下仿生侧线感知研究进展[J]. 水下无人系统学报, 2023, 31(1): 50-67 doi: 10.11993/j.issn.2096-3920.2022-0073
引用本文: 翟宇凡, 熊明磊, 王晨, 等. 水下仿生侧线感知研究进展[J]. 水下无人系统学报, 2023, 31(1): 50-67 doi: 10.11993/j.issn.2096-3920.2022-0073
ZHAI Yu-fan, XIONG Ming-lei, WANG Chen, XIE Guang-ming. A Review on Underwater Perception Based on Bio-inspired Artificial Lateral Line System[J]. Journal of Unmanned Undersea Systems, 2023, 31(1): 50-67. doi: 10.11993/j.issn.2096-3920.2022-0073
Citation: ZHAI Yu-fan, XIONG Ming-lei, WANG Chen, XIE Guang-ming. A Review on Underwater Perception Based on Bio-inspired Artificial Lateral Line System[J]. Journal of Unmanned Undersea Systems, 2023, 31(1): 50-67. doi: 10.11993/j.issn.2096-3920.2022-0073

水下仿生侧线感知研究进展

doi: 10.11993/j.issn.2096-3920.2022-0073
基金项目: 国家自然科学基金项目资助(U22A2062, 12272008, 61633002, 51575005, 61973007)
详细信息
    作者简介:

    翟宇凡(1999-), 男, 在读博士, 主要研究方向为水下仿生机器人、人工侧线系统等

    通讯作者:

    谢广明(1972-), 男, 博士生导师, 教授, 主要研究方向为智能仿生机器人、机器人集群协作等

  • 中图分类号: TP242.6; U661.338

A Review on Underwater Perception Based on Bio-inspired Artificial Lateral Line System

  • 摘要: 侧线是鱼类针对水下环境特有的感知器官, 能够帮助鱼类感知周围水环境信息。受此启发, 设计研制人工侧线系统并应用于水下机器人, 已成为水下无人系统研究的热点之一, 受到国内外学者广泛关注。论文概述水下仿生侧线感知的相关研究进展, 从仿生原理、结构设计、感知功能等角度, 系统地介绍仿生侧线感知的国内外发展现状, 具体介绍了作者团队基于仿箱鲀机器鱼以及仿生侧线系统开展的运动状态估计、姿态保持控制、邻近感知等研究工作。通过对现有研究的分析与总结, 进一步提出了水下仿生侧线感知领域未来的发展方向: 传感器阵列设计优化、自然环境下的流场感知、避障行为与集群行为的实现等。

     

  • 图  1  墨西哥盲鱼

    Figure  1.  Mexican blind fish

    图  2  鱼类侧线神经丘结构与分布

    Figure  2.  Structure and distribution of the neuromasts of the real fish lateral line

    图  3  表面神经丘简化物理模型

    Figure  3.  Simplified physical model of superficial neuromast

    图  4  管道神经丘简化物理模型

    Figure  4.  Simplified physical model of canal neuromast

    图  5  基于不同传感原理的人工侧线传感器

    Figure  5.  Several artificial lateral line sensors based on different sensing principles

    图  6  压强传感器与剪应力传感器分布优化结果

    Figure  6.  Optimization results for the distribution of pressure sensors and shear stress sensors

    图  7  多种人工侧线系统及载体

    Figure  7.  Several artificial lateral line systems and the carriers

    图  8  多种仿生机器鱼及人工侧线系统

    Figure  8.  Several bio-inspired robotic fish and artificial lateral line systems

    图  9  真实的箱鲀鱼

    Figure  9.  Real boxfish

    图  10  2款仿箱鲀机器鱼

    Figure  10.  Two box-fish-inspired robots

    图  11  盘旋运动下基于动力学模型的轨迹估计结果

    Figure  11.  Trajectory estimation results based on dynamic model under spiral motion

    图  12  运动状态估计结果

    Figure  12.  Estimation results of the motion states

    图  13  单尾鳍驱动机器鱼基于双传感器融合方法的攻角控制结果

    Figure  13.  Control results of angle of attack of single-tail driven robotic fish based on dual-sensor fusion method

    图  14  单尾鳍驱动机器鱼基于深度强化学习框架的攻角控制结果

    Figure  14.  Control results of angle of attack of single-tail driven robotic fish based on deep reinforcement learning framework

    图  15  尾鳍后方瞬时涡环图

    Figure  15.  Instantaneous vortex core region behind an individual oscillating caudal fin

    图  16  基于随机森林方法的前方尾鳍摆动幅度估计结果

    Figure  16.  Estimation results of oscillating amplitude of the front caudal fin based on random forest method

    图  17  双鱼领航者-跟随者队形示意图

    Figure  17.  Leader-follower formation diagram of two robotic fishes

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  • 收稿日期:  2022-11-15
  • 修回日期:  2022-12-28
  • 录用日期:  2022-12-28
  • 网络出版日期:  2023-01-17

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