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Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.

The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.

As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms.

This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification.

The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand.

There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs.

The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles.

Laying the acoustic decoupling material on the surface of underwater structures is an effective noise reduction technology. The underwater sound radiation experiment of finite stiffened double cylindrical shell with separate‐sound and decoupled tile is carried out with the aim of finding out the most effective laying condition.

The segmentation power function interpolation method and vertex extreme value envelope continuation method are introduced into basic theory of empirical mode decomposition (EMD). The original measured sound pressure signals are decomposed to intrinsic mode function (IMF) group through EMD, and the high‐frequency components are filtered out. Because the mechanical noise of submarine is mainly at low frequency, the IMFs in low frequency are researched through power spectrum analysis. The noise reduction effects of different separate‐sound and decoupled tile laying conditions are compared.

The sound pressure signal components' amplitudes, periods and phases are obtained through EMD. The test data show that the double cylindrical shell entirely covered with separate‐sound and decoupled tile is the most effective laying condition in noise reduction.

With reference to the case study, this is believed to be the first application of the EMD in sound radiation time‐frequency characteristics of double cylindrical shell. The evaluation of separate‐sound and decoupled tile laying conditions is of great importance in engineering applications.

Motivated by the problems that the positioning error of strap-down inertial navigation system (SINS) accumulates over time and few sensors are available for midwater navigation, this paper aims to propose a self-aided SINS scheme for the spiral-diving human-occupied vehicle (HOV) based on the characteristics of maneuvering pattern and SINS error propagation.

First, the navigation equations of SINS are simultaneously executed twice with the same inertial measurement unit (IMU) data as input to obtain two sets of SINS. Then, to deal with the horizontal velocity provided by one SINS, a delay-correction high-pass filter without phase shift and amplitude attenuation is designed. Finally, the horizontal velocity after processing is used to integrate with other SINS.

Simulation results indicate that the horizontal positioning error of the proposed scheme is less than 0.1 m when an HOV executes spiral diving to 7,000 meters under the sea and it is inherently able to estimate significant sensors biases.

The proposed scheme can provide a precise navigation solution without error growth for spiral-diving HOV on the condition that only IMU is required as a navigation sensor.

The purpose of this paper is to develop a 3D route planner, called 3DPLAN, which employs the Fast‐Pass A* algorithm to find optimum paths in the large grid.

The Fast‐Pass A* algorithm, an improved best‐first search A* algorithm, has a major advantage compared to other search methods because it is guaranteed to give the optimum path.

In spite of this significant advantage, no one has previously used A* in 3D searches. Most researchers think that the computational cost of using A* for 3D route planning would be prohibitive. This paper shows that it is quite feasible to use A* for 3D searches if one employs the new mobility and threat heuristics that have been developed.

This paper reviews the modification of the previous 3DPLAN in the ocean dynamical environment. The test mobility map is replaced with more realistic mobility map that consists of travel times of each grid point to each of its 26 neighbors using the actual current velocity data from the Navy Coastal Ocean Model – East Asian Seas version. Numerical comparison between the A* and genetic algorithms (GA) shows that the A* algorithm has significantly faster running time than GA.

These new heuristics substantially speed up the A* algorithm so that the run times are quite reasonable for the large grids that are typical of 3D searches.

The offshore oil industry is making a profound impact on different parts of the British Isles. It is only three years since Scotland advanced into the oil age with the proving of the Forties field, and a similar process may soon follow around the Celtic Sea area. Many difficult decisions and adjustments have to be made (both onshore and offshore) in the fields of employment, planning, the provision of infrastructure, marine engineering, offshore supply and transport. In such a fast‐moving industry information provision is of paramount importance, but the speed of development makes it all the more difficult to achieve. This paper attempts to aid decision making by showing what information is available in published form, and by discussing some of the available sources of unpublished data. The writer's own attempts to fill some of the information gaps are discussed and some basic acquisitions are recommended for anyone starting to build up a collection on offshore oil.

Utilization of electromagnetic wave (EMW) sensors in an underwater environment has the potential to increase the data rate compared to acoustic-based sensors because of the ability to use larger signal bandwidth. Nevertheless, EMW signals has the drawback of large signal attenuation in underwater, attributed to the high relative permittivity and conductivity of water compared to the atmosphere, hence employment of wide signal bandwidth is necessary to balance the data rate-attenuation trade-off. The purpose of this paper is to analyze the characteristics of both narrowband and wideband EMW signal propagation underwater and devise a path loss model for both cases.

Path loss measurement was conducted using a point-to-point configuration in a laboratory water tank while transmitting narrowband and wideband signals between a pair of wideband underwater antennas. The wideband underwater antennas use buffer-layer structures as the impedance matching layer to optimize the antenna performance when operating underwater. The path loss for narrowband signal was modeled using a multi-layer propagation equation in lossy medium considering losses at the medium boundaries. For the case of the wideband signal, a modified version of the model introducing power integration over bandwidth is adopted. These models were formulated through numerical simulations and verified by measurements.

The measured narrowband path loss marked an 80 dB attenuation using 800 MHz at 2 m distance. The proposed narrowband model agrees well with the measurements, with approximately 3 dB modeling error. Utilization of the proposed wideband path loss model resulted in a reduction of the gradient of the path loss curve compared to the case of the narrowband signal. The measured wideband path loss at 2 m distance underwater was approximately −65 dB, which has been shown to enable a working signal-to-noise ratio of 15 dB. This proves the potential of realizing high data rate transmission using the wideband signal.

The paper proposed a wideband propagation model for an underwater EMW sensor network, using power integration over bandwidth. The effectiveness of using wideband EMW signals in reducing path loss is highlighted, which is seldom discussed in the literature. This result will be of useful reference for using wideband signals in designing a high data rate transmission system in underwater wireless sensor networks, for example, in link budget, performance estimation and parameter design of suitable transmission scheme.

This paper aims to provide details of underwater robot technology and its applications.

Following an introduction, this article first discusses remotely operated vehicle (ROV) technology and applications and then considers their use in the emerging field of deep-sea mining. It then discusses autonomous underwater vehicle (AUV) technology and its applications, including sub-sea gliders. Finally, brief concluding comments are drawn.

ROVs were first developed in the 1950s for military applications. They are now widely used by the offshore oil and gas sector and other industries and are being developed for deep-sea mining. AUV technology has progressed rapidly in recent years and AUVs, including sub-sea gliders, are now emerging from their original role in oceanographic research and finding growing uses in the defence and offshore energy sectors.

This provides a detailed insight into underwater robot technologies, products and applications.

The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality.

Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process.

Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding.

A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.