As one of the important means for human beings to understand and develop the ocean, the application of underwater robots in marine science and engineering has attracted more and more attention. Underwater robots are divided into two categories, one is remotely operated underwater vehicle (ROV) and the other is autonomous underwater vehicle (AUV). Autonomous submersibles do not carry cables. Its energy is installed on the robot. Its task execution process is controlled by computer. Complete the task independently according to the robot program. Because there is no cable constraint, further analysis data can be collected after returning. AUV has the characteristics of autonomous navigation and large-scale observation. This paper presents a method based on convolution neural network, which realizes the effective perception and recognition of underwater environment. Through the ROS operating system, we integrate the control and environmental perception of the underwater robot, and design an underwater robot that can avoid obstacles and recognize autonomously.


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    Title :

    Obstacle avoidance and scene switching recognition for autonomous underwater vehicles based on convolutional neural networks


    Contributors:
    Zhang, Yuhan (author) / Ge, Weili (author)

    Conference:

    International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022) ; 2022 ; Hohhot,China


    Published in:

    Proc. SPIE ; 12454


    Publication date :

    2022-11-23





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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