Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

Abstract

To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment.

A Low-Cost and High-Precision Underwater Integrated Navigation System

“…Duecker et al [19] uses a camera to recognize the AprilTag marking system and obtain complete relative 3D pose information underwater. Yu et al [20] proposed an ArUco code location determination method for AUV charging, based on deep learning and the law of refraction. Most of the above methods have been tested only in clean pools and simulated environments.…”