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Autonomous Underwater Vehicle Control for Fishnet Inspection in Turbid Water Environments

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  • Robot and Applications
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Abstract

Fisheries are essential for the economic supply of proteins. Detecting damaged fishnets using autonomous underwater vehicles (AUVs) may be an efficient and safe solution for avoiding dangers to human divers. However, in turbid underwater environments, visibility is significantly degraded by floating particles that cause light attenuation, which is one of the main problems for accurate underwater inspection by optical cameras. To obtain clear images for net inspection, we propose an AUV pose control strategy for fish farming net inspection in turbid water, based on the mean gradient feature over the partial or entire image. To alleviate the laborious human process of setting the desired set-point for distance control, a convolutional neural network (CNN) is trained offline using a supervised learning method and combined with a controller. The proposed method can maintain a relatively constant relative pose with respect to a fishnet, which is sufficient to acquire clear net images in turbid water and check whether a part of the net is damaged or not. Experimental results in both swimming pools and real fish farm environments demonstrated the effectiveness of the proposed methods.

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Authors and Affiliations

  1. School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), 123, Cheomdangwagi-ro, Buk-gu, Gwangju, Korea

    Hoosang Lee, Daehyeon Jeong & Hongje Yu

  2. School of Integrated Technology and the Artificial Intelligence Graduate School Program, Gwangju Institute of Science and Technology (GIST), 123, Cheomdangwagi-ro, Buk-gu, Gwangju, Korea

    Jeha Ryu

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  1. Hoosang Lee
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  2. Daehyeon Jeong
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Correspondence to Jeha Ryu.

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This work was supported by GIST Research Institute (GRI) grant funded by the GIST.

Hoosang Lee received his B.S. degree in robotics from Kwangwoon University, Seoul, Korea in 2018, and the M.S. degree in intelligent robotics technology from Gwangju Institute of Science and Technology (GIST), Gwangju, Korea in 2020. His research interests include robot control and robot learning.

Daehyeon Jeong received his B.S. degree in mechanical engineering from Korea University, Seoul, Korea in 2013, and the M.S. degree in mechatronics, and the Ph.D. degree in intelligent robotics technology from Gwangju Institute of Science and Technology, Gwangju, Korea, in 2015 and 2022, respectively. His research interests include artificial intelligence and autonomous driving.

Hongje Yu received his B.S. degree in mechanical engineering, and the M.S. degree in intelligent robotics technology from Gwangju Institute of Science and Technology (GIST) in 2018 and 2020, respectively. His research interests include control engineering.

Jeha Ryu received his B.S., M.S., and Ph.D. degrees in mechanical engineering from Seoul National University, Seoul, Korea, in 1982, from Korea Advanced Institute of Science and Technology (KAIST), in 1984, and from University of Iowa, in 1991, respectively. He was a professor in the Department of Mechatronics, and now in the School of Integrated Technology at Gwangju Institute of Science and Technology (GIST). His research interests include robot/vehicle kinematics, dynamics, control, haptics, virtual reality, and artificial intelligence.

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Lee, H., Jeong, D., Yu, H. et al. Autonomous Underwater Vehicle Control for Fishnet Inspection in Turbid Water Environments. Int. J. Control Autom. Syst. 20, 3383–3392 (2022). https://doi.org/10.1007/s12555-021-0357-9

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  • DOI: https://doi.org/10.1007/s12555-021-0357-9

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