Design and implementation of underwater intelligent recognition and autonomous grasp vehicle

被引：0
|
作者：
Gao T.-M. [1 ,2 ]
Yan J. [1 ,2 ]
You K.-L. [1 ]
Zhang L. [1 ]
Lin J.-S. [1 ]
Luo X.-Y. [1 ]
机构：
[1] Institute of Electrical Engineering, Yanshan University, Hebei, Qinhuangdao
[2] Key Laboratory of Ocean Observation Technology, MNR, Tianjin
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2022年 / 39卷 / 11期
基金：
中国国家自然科学基金;
关键词：
deep learning; grasp; recognition; underwater vehicle;
D O I：
10.7641/CTA.2022.11087
中图分类号：
学科分类号：
摘要：
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. © 2022 South China University of Technology. All rights reserved.
引用
收藏
页码：2074 / 2083
页数：9
相关论文
共 24 条
  • [1] WU W, YIN Y, WANG X, Et al., Face detection with different scales based on Faster R-CNN, IEEE Transactions on Cybernetics, 49, 11, pp. 4017-4028, (2019)
  • [2] XU F, DING X, PENG J, Et al., Real-time detecting method of marine small object with underwater robot vision, Proceedings of Oceans, pp. 1-4, (2018)
  • [3] HUANG H, ZHOU H, YANG X, Et al., Faster R-CNN for marine organisms detection and recognition using data augmentation, Neurocomputing, 337, 1, pp. 372-384, (2019)
  • [4] REDMON J, DIVVALA S, GIRSHICK R, Et al., You only look once: Unified, real-time object detection, Proceedings of IEEE Conference on Computer Vision & Pattern Recognition, pp. 1-10, (2016)
  • [5] YOUSSEF W, HUSSAM E, ALI F, Et al., YOLO fish detection with Euclidean tracking in fish farms, Journal of Ambient Intelligence and Humanized Computing, 12, 1, pp. 5-12, (2021)
  • [6] BOCHKOVSKIY A, WANG C, LIAO H., YOLOv4: Optimal speed and accuracy of object detection, (2020)
  • [7] CUI S, WANG Y, WANG S, Et al., Real-time perception and positioning for creature picking of an underwater vehicle, IEEE Transactions on Vehicular Technology, 69, 4, pp. 3783-3792, (2020)
  • [8] XU J, CHEN X, SONG X, Et al., Target recognition and location based on binocular vision system of UUV, Proceedings of Chinese Control Conference, pp. 3959-3963, (2015)
  • [9] DAI Y, YU S, YAN Y, Et al., An EKF-based fast tube MPC scheme for moving target tracking of a redundant underwater vehicle-manipulator system, IEEE/ASME Transactions on Mechatronics, 24, 6, pp. 2803-2814, (2019)
  • [10] YAN J, LI X, LUO X, Et al., Integrated localization and tracking for AUV with model uncertainties via scalable sampling-based reinforcement learning approach, IEEE Transactions on Systems, Man, and Cybernetics: Systems, (2021)
123