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

Yuhan Zhang; Weili Ge

doi:10.1117/12.2658894

23 November 2022 Obstacle avoidance and scene switching recognition for autonomous underwater vehicles based on convolutional neural networks

Yuhan Zhang, Weili Ge

Author Affiliations +

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 1245421 (2022) https://doi.org/10.1117/12.2658894
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

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.

Citation Download Citation

Yuhan Zhang and Weili Ge "Obstacle avoidance and scene switching recognition for autonomous underwater vehicles based on convolutional neural networks", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 1245421 (23 November 2022); https://doi.org/10.1117/12.2658894

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KEYWORDS

Robots

Convolution

Convolutional neural networks

Sensors

RGB color model

Computing systems

Image processing

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