Design and implementation of underwater intelligent recognition and autonomous grasp vehicle

This paper presents an underwater intelligent recognition and autonomous grasp vehicle for the fishing of precious seafood. Firstly, we employ the YOLOv4-tiny network to off-line train the images of precious seafood, through which monocular-binocular adaptive switch and multi-target selection algorithms are conducted to achieve on-line recognition and persistent localization. Moreover, the depth information of the underwater vehicle is acquired by the fusion of sonar and depth sensor, and fuzzy proportional integral differential based depth-fixed and grasp controllers are designed respectively to guarantee effective feedback for the depth information during the procedures of target localization and grasp. The employed seafood recognition algorithm has the advantages of high real time and low complexity. On the other hand, the proposed depth-fixed and grasp controllers do not rely on complex system model, which can suit the target accurate grasp in different sea states. Finally, the experiments are conducted to verify the effectiveness of the developed approach.

Recommended citation: T. Gao, J. Yan, K. You, L. Zhang, J. Lin, and X. Luo, Design and implementation of underwater intelligent recognition and autonomous grasp vehicle. Control Theory and Technology, vol. 39, no. 11, pp. 2074~2083, 2022.
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