Vision-Based Obstacle Avoidance and Formation Control for Underwater Robotic Fish
文献类型：期刊论文
| 作者 | Han, Jiarong4; Huang, Rui4; Yuan, Xiangqing4; Liu, Yu3; Yin B(银波)1,2; Zou, Suli4; Ma, Zhongjing4 |
| 刊名 | IEEE ROBOTICS AND AUTOMATION LETTERS
 |
| 出版日期 | 2025-10-01 |
| 卷号 | 10期号:10页码:10442-10449 |
| 关键词 | Robots Fish Collision avoidance Robot kinematics Visualization Accuracy Hydrodynamics Formation control Feature extraction Servomotors Biologically-inspired robots marine robotics computer vision formation control robotic fish |
| ISSN号 | 2377-3766 |
| DOI | 10.1109/LRA.2025.3599014 |
| 通讯作者 | Ma, Zhongjing(mazhongjing@bit.edu.cn) |
| 英文摘要 | This letter aims to enhance the autonomy of bionic robotic fish formation in executing underwater tasks by integrating visual recognition and control systems. Firstly, we design an adaptive image enhancement (AIE) module that integrates a hyperparameter neural network (HPNN) into the YOLOv8 framework, which improves recognition performance under low-light and high-interference underwater conditions, enhancing the obstacle perception capability of underwater robots. Secondly, considering the underactuated and nonlinear hydrodynamic characteristics of the robotic fish system, a formation heading and speed controller with constrained control freedom is designed. This involves establishing a side-slip dynamics model for the robotic fish, analyzing its nonlinear hydrodynamics, and proving the Lyapunov stability of the controller. Finally, the synergistic efficacy of the visual and control systems is validated through a series of experiments, including target tracking, target frame traversal, formation maintenance and reconstruction, and formation obstacle avoidance. These experiments demonstrate that the collaboration of the proposed perception and control modules significantly enhances the capability of the robotic fish formation to autonomously undertake complex underwater tasks. |
| 分类号 | 二类/Q1 |
| 资助项目 | National Natural Science Foundation of China[U22A2048] ; National Natural Science Foundation of China[12272383] ; National Natural Science Foundation of China[62373051] ; BIT Research and Innovation Promoting Project[2024YCXY020] ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| WOS记录号 | WOS:001561066000010 |
| 资助机构 | National Natural Science Foundation of China ; BIT Research and Innovation Promoting Project ; Fundamental Research Funds for the Central Universities |
| 其他责任者 | Ma, Zhongjing |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/103641]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing 100190, Peoples R China; 3.Chinese Acad Sci, Inst Acoust, Beijing 100190, Peoples R China; 4.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Han, Jiarong,Huang, Rui,Yuan, Xiangqing,et al. Vision-Based Obstacle Avoidance and Formation Control for Underwater Robotic Fish[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2025,10(10):10442-10449. |
| APA | Han, Jiarong.,Huang, Rui.,Yuan, Xiangqing.,Liu, Yu.,银波.,...&Ma, Zhongjing.(2025).Vision-Based Obstacle Avoidance and Formation Control for Underwater Robotic Fish.IEEE ROBOTICS AND AUTOMATION LETTERS,10(10),10442-10449. |
| MLA | Han, Jiarong,et al."Vision-Based Obstacle Avoidance and Formation Control for Underwater Robotic Fish".IEEE ROBOTICS AND AUTOMATION LETTERS 10.10(2025):10442-10449. |
入库方式： OAI收割
来源：力学研究所
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