Vision-Based Obstacle Avoidance and Formation Control for Underwater Robotic Fish

Abstract

This letter aims to enhance the autonomy of bionic robotic fish formation in executing underwater tasks by integrating visual recognition and control systems. Firstly, we design an adaptive image enhancement (AIE) module that integrates a hyperparameter neural network (HPNN) into the YOLOv8 framework, which improves recognition performance under low-light and high-interference underwater conditions, enhancing the obstacle perception capability of underwater robots. Secondly, considering the underactuated and nonlinear hydrodynamic characteristics of the robotic fish system, a formation heading and speed controller with constrained control freedom is designed. This involves establishing a side-slip dynamics model for the robotic fish, analyzing its nonlinear hydrodynamics, and proving the Lyapunov stability of the controller. Finally, the synergistic efficacy of the visual and control systems is validated through a series of experiments, including target tracking, target frame traversal, formation maintenance and reconstruction, and formation obstacle avoidance. These experiments demonstrate that the collaboration of the proposed perception and control modules significantly enhances the capability of the robotic fish formation to autonomously undertake complex underwater tasks.