The accurate target detection under different environmental conditions and the real-time target positioning are vital for the successful accomplishment of underwater missions of Remotely operated vehicles (ROVs). In this paper, we propose a vision-based underwater target detection and positioning approach to detect and estimate the position and attitude of artificial underwater targets. The proposed approach is composed of an underwater target detection algorithm YOLO-T and a target positioning algorithm. Firstly, we modify the structure of YOLOv5 algorithm using Ghost module and SE attention module to improve the calculation time of target detection. Secondly, a series of image processing operations are performed on the improved YOLOv5 detection results to increase the detection accuracy. Thirdly, a cooperative marker is designed as the artificial underwater target, and the corresponding positioning algorithm is presented to calculated the position and attitude of the target according to the geometric information of the designed marker. We test our approach through experimental tests a water tank in the laboratory, an anechoic tank and the sea trial in the Huanghai sea in China. The results demonstrate the accurate performance of the proposed detection and positioning method.

It is of great significance to measure real-time surface vessel magnetic field characteristics based on underwater vehicle equipped with magnetometers. In order to obtain the position information of the vehicle at the bottom of the vessel, it is crucial to estimate its speed and apply it to the auxiliary navigation of acoustic positioning systems. In this paper, we proposed a novel method for estimating moving velocity of an underwater vehicle by processing anomaly magnetic field signals measured by two magnetometers. Firstly, we established a detecting model for anomaly magnetic field of a fixed magnetic object using two magnetometers at different positions on the same straight line. Theoretical analysis based on the detecting model indicated that the speed of underwater vehicle can be obtained by fusing the spatial distance of the magnetometers and the time interval of the anomaly magnetic field waveforms. Then, the sparrow search algorithm was introduced to estimate the time interval considering that this optimization algorithm has the advantage of simple structure and fast convergence. Finally, computer simulation and real-word experiments have been conducted to verify the performance of the method. The results demonstrated that the method can calculate speed of the magnetometers correctly with an error of less than 2% and calculation time less than 3s. The features of easy implementation and low computational complexity make the proposed method a potential candidate for the application in the underwater assisted navigation and other fields.

Underwater wireless communication; underwater acoustic communication; underwater optical communication; underwater electromagnetic communication; underwater magnetic induction communication; equipment system

The internal hydroforming of large stainless-steel eggshells from a stepped preform is investigated in this paper. The nominal major and minor axes of the eggshell were 1537 and 1070 mm, respectively. The stepped preform was fabricated from thin-walled (1.9 mm thick) stainless-steel sheets and comprised twelve conical segments inscribed inside the target eggshell. The preform was then hydroformed, and its wall thickness and shape were measured. The yield load distribution and material hardening of the hydroforming process were investigated analytically. Nonlinear finite-element analyses were employed to further investigate hydroforming behaviors and the effect of weld lines on hydroforming. The experimental, numerical, and analytical results were consistent. The results confirm that, during the hydroforming process, considerable springback occurs for large eggshells, which greatly affects forming precision. However, this effect can be reduced by accounting for the strengthening effect of weld lines.

Deep sea; exploration and residence; perception and detection; operation platform; sensor

This paper tackles the highly challenging problem of automatic berthing for autonomous surface vessels (ASVs), encompassing trajectory planning, trajectory tracking, and collision avoidance. Firstly, a novel A* algorithm integrated with a quasi-uniform B-spline and quadratic interpolation method (A*QB) is proposed for generating a smooth trajectory from the initial position to the berth, utilizing an offline-generated scaled map. Secondly, the optimal nonlinear model predictive control (NMPC)-based trajectory-tracking framework is established, incorporating the model’s uncertainty, the input saturation, and environmental disturbances, based on a 3-DOF model of a ship. Finally, considering the collision risks during port berthing, a COLREGs-based collision avoidance method is investigated. Consequently, a novel trajectory-tracking and COLREGs-based collision avoidance (TTCCA) scheme is proposed, ensuring that the ASV navigates along the desired trajectory, safely avoids both static and dynamic obstacles, and successfully reaches the berth. To validate the TTCCA approach, numerical simulations are conducted across four scenarios with comparisons to existing methods. The experimental results demonstrate the effectiveness and superiority of the proposed scheme.