Online Velocity Estimation of a Robotic Fish Using Artificial Lateral Line System with Velocity-Decoupling Sensing Ability

The robotic fish has attracted widespread research interest over the past few decades, due to its outstanding agility and environmental friendliness. And the sensing ability of under- water environments is crucial for the robotic fish to accomplish various underwater tasks. Inspired by the lateral line of real fish, many types of artificial lateral line (ALL) sensors have been pro- posed, including pressure-based sensors and deformation-based sensors. However, currently these types of ALL sensors mounted on robotic fish are susceptible to the interference from robotic fish’s self-motions such as yaw motion and pitch motion, as well as the unavoidable vortices around the robotic fish. To address the above issues, a deformation-based magnetic ALL sensor capable of flow velocity-decoupling sensing is proposed, which can be used to measure the swimming speed of the robotic fish while suppressing the aforementioned noise. Besides, an ALL array is designed and mounted on both sides of a robotic fish, enabling the measurement of its swimming speed under both rectilinear and turning motion, with a mean absolute error (MAE) of 0.0153 m/s and 0.0125 m/s, respectively. Based on this, the ALL array is applied for trajectory estimation of the robotic fish, and the MAE of trajectory estimation under rectilinear and turning motion is 0.0600 m and 0.0730 m, respectively. Received 26 April 2025; accepted 7 August 2025. Date of publication 20 Au- gust 2025; date of current version 29 August 2025. This letter was recommended for publication by Associate Editor P. Manoonpong and Editor C. Laschi upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 52205424 and Grant 524B2062, in part by the State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology under Grant P2024-010, in part by the China National Postdoctoral Program for Innovative Talents under Grant BX20240321, and in part by Zhejiang Provincial Teams of Leading Talents in Innovation and Entrepreneurship under Grant 2024R01002. (Corresponding authors: Chengqian Zhang; Peng Zhao.) Jiarui He, Huangzhe Dai, Daofan Tang, Chengfeng Pan, and Peng Zhao are with the State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China, and also with the Zhejiang Key Laboratory of Additive Manufacturing Technol- ogy and Equipment, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China (e-mail: hejiarui@zju.edu.cn; 22125067@zju.edu.cn; 21925024@zju.edu.cn; cfpan@zju.edu.cn; pengzhao@zju.edu.cn). Yan Zhou is with the College of Design and Engineering, National University of Singapore, Singapore 119077 (e-mail: e1546952@u.nus.edu). Chengqian Zhang is with the State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang Univer- sity, Hangzhou 310058, China, also with the Zhejiang Key Laboratory of Additive Manufacturing Technology and Equipment, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China, and also with the State Key Laboratory of Materials Processing and Die & Mould Technology, HuazhongUniversityofScienceandTechnology,Wuhan430074,China(e-mail: zhangcq@zju.edu.cn). This article has supplementary downloadable material available at https://doi.org/10.1109/LRA.2025.3601030, provided by the authors. Digital Object Identifier 10.1109/LRA.2025.3601030