Autonomous Inspection Strategies and Simulation for Large Aquaculture Net Cages Based on Deep Visual Perception

Abstract

In China, a single large deep-sea net cage can raise nearly one million fish. If the fish net is damaged and the fish escape, it can lead to significant economic losses and ecological damage. Therefore, the inspection and maintenance of deep-sea aquaculture net cages are very important. Currently, the inspection of fish nets relies primarily on manual remote control, and underwater positioning often uses ultra-short baseline systems, which depend on specialized personnel and have high costs. This paper proposes an autonomous inspection strategy for large aquaculture net cages based on deep visual perception. It utilizes stereo cameras to identify the relative distance and attitude angles between the robot and the sides of fish net as well as the fish net ahead. A PID method is employed to control the underwater autonomous net patrol robot to conduct operations with fixed depth, fixed distance, and attitude holding around the net. By integrating the Gazebo physical simulation platform with the ROS (Robot Operating System), a simulation environment for the underwater autonomous net patrol robot was constructed. The study investigated the inspection performance of the robot under different speed conditions, both in still water and considering current conditions. By comparing the actual operating trajectory with the expected trajectory, the proposed autonomous inspection strategy was validated. Moreover, the study examined the operation state under sudden disturbance forces, where the robot deviated six meters from the net cage and rotated 70 degrees. The simulation results indicate that under this control strategy, the robot can quickly recover its desired pose and continue executing the inspection task.