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Conducting sea-going ocean science no longer needs to be limited to the number of berths on a ship given that telecommunications, computing, and networking technologies onboard ships have become familiar mechanisms for expanding scientists' reach from onshore. The oceanographic community routinely works with remotely operated vehicles (ROVs) and pilots to access real-time video and data from the deep sea, while onboard a ship. The extension of using an ROV and its host vessel's live-streaming capabilities has been popularized for almost 3 decades as a telepresence technology. Telepresence-enabled vessels with ROVs have been employed for science, education, and outreach, giving a greater number of communities viewing access to ocean science. However, the slower development of technologies and social processes enabling sustained real-time involvement between scientists on-ship and onshore undermines the potential for broader access, which limits the possibility of increasing inclusivity and discoveries through a diversity of knowledge and capabilities. This article reviews ocean scientists' use of telepresence for ROV-based deep-sea research and funded studies of telepresence capabilities. The authors summarize these studies findings and conditions that lead to defining the use of telepresence-enabled vessels for "remote science at sea." Authors define remote science at sea as a type of ocean expedition, an additional capability, not a replacement for all practices by which scientists conduct ocean research. Remote science for ocean research is an expedition at-sea directed by a distributed science team working together from at least two locations (on-ship and onshore) to complete their science objectives for which primary data is acquired by robotic technologies, with connectivity supported by a high-bandwidth satellite and the telepresence-enabled ship's technologies to support the science team actively engaged before, during, and after dives across worksites. The growth of productive ocean expeditions with remote science is met with social, technical, and logistical challenges that impede the ability of remote scientists to succeed. In this article, authors review telepresence-enabled ocean science, define and situate the adjoined model of remote science at sea, and some infrastructural, technological and social considerations for conducting and further developing remote science at sea. [ABSTRACT FROM AUTHOR]

An important process for underwater acoustic signal is noise reduction. In ocean exploration and the military, the minimization of noise in the underwater environment is essential to provide a significant impact in our society. While considering the different acoustic channels and complexity of marine environment, the noise reduction process in acoustic signals is always difficult. Owing to these complexities, an advanced noise reduction mechanism for underwater acoustic signal denoising is performed using adaptive deep learning method is developed. The proposed model involves finding and analyzing the noises present in the underwater acoustic signal, which is helpful for underwater target detection, recognition and acoustic communication quality. Here, a Advanced Recurrent Neural Network with Novel Loss Function (ARRNN-NLF) is implemented for reducing noises in underwater acoustic signals. Hence, the input signal is given to the reduction process where the ARRNN-NLF network is utilized for densification. The high-order nonlinear features from the original signal are extracted and converted into subvectors with fixed lengths based on temporal dimension. Finally, the denoised signal is obtained from the developed ARRNN with a minimum loss between the predicted and target output. Here, the parameters from ARRNN-NLF are optimized by the Enhanced Osprey Optimization Algorithm (EOOA) for enhancing the denoising model. The resultant results are evaluated by diverse conventional denoising models to prove efficiency. [ABSTRACT FROM AUTHOR]

The rapid development of ocean exploration and underwater robot technology has put forward new requirements for underwater sensing methods, which can be used for hydrodynamic characteristics perception, underwater target tracking, and even underwater cluster communication. Here, inspired by the specialized undulated surface structure of the seal whisker and its ability to suppress vortex‐induced vibration, a multidirectional hydrodynamic sensor based on biomimetic whisker array structure and magnetic 3D self‐decoupling theory is introduced. The magnetic‐based sensing method enables wireless connectivity between the magnetic functional structures and electronics, simplifying device design and endowing complete watertightness. The 3D self‐decoupling capability enables the sensor, like a seal or other organisms, to perceive arbitrary whisker motions caused by the action of water flow without complex calibration and additional sensing units. The whisker sensor is capable of detecting a variety of hydrodynamic information, including the velocity (RMSE < 0.061 m s−1) and direction of the steady flow field, the frequency (error < 0.05 Hz) of the dynamic vortex wake, and the orientation (error < 7°) of the vortex wake source, demonstrating its extensive potential for underwater environmental perception and communication, especially in deep sea conditions. [ABSTRACT FROM AUTHOR]

Underwater optical communication (UOC) based on the novel photoelectrochemical (PEC) type photodetector has garnered substantial interest in the field of marine resource exploration and ocean investigation due to its strong operability, self‐powered capability, and large sensitive area in harsh underwater environments. To achieve high sensitivity, fast transmission speed, and broadband response characteristics, vertical‐GaS nanosheets‐based PEC photodetectors are prepared by chemical vapor deposition for UOC application. The GaS based PEC photodetector exhibits excellent self‐powered photodetection and a broad UV–vis photoresponse range as well as high stability performance in both Na2SO4 electrolyte and seawater. Notably, it also demonstrates an exceptional photoresponsivity of 11 mA W−1 coupled with a fast response and recovery time of 12.9 and 13 µs which are superior to those of 2D layered materials and conventional wide‐bandgap semiconductors. These improvements are mainly due to the large surface area, short diffusion length, as well as high carrier mobility of vertically‐oriented GaS nanosheets. Strikingly, the constructed GaS photodetector showcases a broad bandwidth exceeding the −3 dB cutoff frequency of 55.3 kHz during UOC testing, successfully enabling transmission and decoding of character information. This work establishes the groundwork for developing high‐performance, self‐powered UV–vis photodetectors adaptable to UOC using PEC technology. [ABSTRACT FROM AUTHOR]

The underwater Internet of Things (UIoT) and remote sensing are significant for biodiversity preservation, environmental protection, national security, disaster assistance, and technological innovation. Assigning tasks to autonomous underwater vehicles (AUVs) is a fundamental challenge in underwater technology and exploration. Remote sensing and AUVs are vital for pollution detection, disaster prevention, marine observation, and ocean monitoring. This work presents an optimized network connectivity using a multi-attribute decision-making approach for underwater IoT deployment. A feature engineering approach highlights the significant characteristics of underwater things, incorporating remote sensing data, and a multi-objective optimization method is used to select optimal UIoT for effective task allocation in deep-sea environments. A balance between data transmission, energy economy, and operational performance is necessary for efficient task distribution. Effective communication algorithms and protocols are needed to maintain environmental sustainability, protect marine ecosystems, and improve underwater monitoring enhanced by remote sensing technologies. Multicriteria decision-making (MCDM) is beneficial for addressing various challenges in underwater technology, considering factors such as mission objectives, energy efficiency, environmental conditions, vehicle performance, safety, and much more. The proposed criteria importance through intercriteria correlation (CRITIC) methodology will assess technical competencies like communication, resilience, navigation, and safety in an underwater environment, leveraging remote sensing and aiding decision-makers in selecting appropriate undersea devices and vehicles for enhancing communication and transportation. This method prioritizes characteristics and aligns them with specific objectives, improving decision-making quality in the marine environment. [ABSTRACT FROM AUTHOR]

Aquatic mobile robots are gaining attention for their potential to revolutionize marine monitoring and exploration within the Ocean Internet of Things. A significant challenge for these untethered robots, especially in remote areas, is achieving energy autonomy. This work presents an innovative self‐sustaining energy system for compact aquatic robots, inspired by biological digestion. Utilizing microbial fuel cell (MFC) technology, organic materials found in aquatic environments are converted into electricity through catalytic redox reactions. To extend the MFC's lifespan, spore‐forming Bacillus subtilis is used as the anodic biocatalyst, leveraging its ability to endure harsh conditions and reactivate in favorable environments, thus enhancing the MFC's longevity. To ensure a steady supply of organic substrates for microbial viability, a biomimetic Janus membrane with asymmetric surface wettability is integrated, enabling selective substrate intake. Additionally, stability mechanisms inspired by water striders allow the robot to move efficiently across water surfaces. The robot mimics the water strider's movement using a motor powered by microbial metabolism, fueled by organic nutrients via the Janus membrane. This study demonstrates the feasibility of using natural processes for technological advancement, setting new benchmarks in the design of autonomous systems. [ABSTRACT FROM AUTHOR]

There is evidence from the near‐infrared observations of space missions of the presence of carbonates on the surface of several ocean worlds. However, their genesis remains unresolved. We investigate the hypothesis that these carbonates may be in the form of clathrites assuming that clathrate hydrates are stable phases in the crust and ocean of these ocean worlds. In order to support this, we studied a sample of a potential clathrite from the Hydrate Ridge cold seep (Cascadia Subduction Zone), the carbonate rock fossil of clathrate hydrates, as a terrestrial analogue. We characterised the mineralogy and texture of the sample by using a coupled confocal Raman microscope and scanning electron microscopy instrument with the aim of identifying possible geo‐ and biosignatures, which could be relevant for future missions of exploration to ocean worlds and Mars. Our results show that aragonite is the dominant mineral phase in the clathrite sample, but Mg‐calcite and dolomite were also identified. These three carbonates constitute a pattern related to clathrate hydrate formation and dissociation processes. Dolomite was defined as a biosignature of gas hydrate microbiomes because it was integrated within Mg‐calcite grains precipitated after clathrate hydrate dissociation. Nevertheless, no spectral changes were observed in Raman bands of carbonate minerals that would indicate the influence of clathrate hydrates in their genesis. We also observed that Raman band positions of the associated framboidal pyrites are a characteristic signature of the associated framboid‐like texture because its potential as biosignature may only be attributed by biochemical analysis. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in underwater navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL), hindering the development of low-cost vehicles. Micro Electro Mechanical System Inertial Measurement Units (MEMS IMUs) are widely used in industry due to their low cost and can output acceleration and angular velocity, making them suitable as an Attitude Heading Reference System (AHRS) for low-cost vehicles. However, poorly calibrated MEMS IMUs provide an inaccurate angular velocity, leading to rapid drift in orientation. In underwater environments where AUVs cannot use GPS for position correction, this drift can have severe consequences. To address this issue, this paper proposes Underwater Gyros Denoising Net (UGDN), a method based on dilated convolutions and LSTM that learns and extracts the spatiotemporal features of IMU sequences to dynamically compensate for the gyroscope's angular velocity measurements, reducing attitude and heading errors. In the experimental section of this paper, we deployed this method on a dataset collected from field trials and achieved significant results. The experimental results show that the accuracy of MEMS IMU data denoised by UGDN approaches that of fiber-optic SINS, and when integrated with DVL, it can serve as a low-cost underwater navigation solution. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV's dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments. [ABSTRACT FROM AUTHOR]

Due to the inherent unpredictability of marine trial conditions, safety issues in the launching and recovery process are commonplace. For these issues, digital twin (DT) maintenance offers a novel idea. In this paper, a blended-wing-body underwater glider skeleton is selected as a research object. And a simple demo is created to investigate the major technologies that may be involved in DT maintenance. According to this specimen, the necessary development environments are developed to obtain physical information and produce a DT model in cyberspace respectively. An attitude sensor is used as the physical data collector, while real-time structural field prediction and virtual reality visualisation are employed. By using the attitude angles and structural data, it is possible to achieve real-time monitoring of the skeleton strength. Through the means of this straightforward case study, the key technologies are supported and can be applied to far more complex inquiries. [ABSTRACT FROM AUTHOR]

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, they lag behind CNNs in handling fine local features, especially when dealing with underwater images containing complex and delicate structures. In order to tackle this challenge, we propose a refined transformer model by improving the feature blocks (dilated transformer block) to more accurately compute attention weights, enhancing the capture of both local and global features. Subsequently, a self-supervised method (a local and global blind-patch network) is embedded in the bottleneck layer, which can aggregate local and global information to enhance detail recovery and improve texture restoration quality. Additionally, we introduce a multi-scale convolutional block attention module (MSCBAM) to connect encoder and decoder features; this module enhances the feature representation of color channels, aiding in the restoration of color information in images. We plan to deploy this deep learning model onto the sensors of underwater robots for real-world underwater image-processing and ocean exploration tasks. Our model is named the refined transformer combined with convolutional block attention module (RT-CBAM). This study compares two traditional methods and six deep learning methods, and our approach achieved the best results in terms of detail processing and color restoration. [ABSTRACT FROM AUTHOR]

The high-precision magnetic survey system is crucial for ocean exploration. However, most existing systems face challenges such as high noise levels, low sensitivity, and inadequate magnetic compensation effects. To address these issues, we developed a high-precision magnetic survey system based on the manned submersible "Deep Sea Warrior" for deep-ocean magnetic exploration. This system incorporates a compact optically pumped cesium (Cs) magnetometer sensor to measure the total strength of the external magnetic field. Additionally, a magnetic compensation sensor is included at the front end to measure real-time attitude changes of the platform. The measured data are then transmitted to a magnetic signal processor, where an algorithm compensates for the platform's magnetic interference. We also designed a deep pressure chamber to allow for a maximum working depth of 4500 m. Experiments conducted in both indoor and field environments verified the performance of the proposed magnetic survey system. The results showed that the system's sensitivity is ≤0.5 nT, the noise level of the magnetometer sensor is ≤1 pT/√Hz at 1 Hz, and the sampling rate is 10 Hz. The proposed system has potential applications in ocean and geophysical exploration. [ABSTRACT FROM AUTHOR]

Bubble detection in water plays important roles in human exploration and management of the ocean. This research presents a detection technique based on laser polarization dual-mode fusion, aiming at solving the difficulties of light scattering intensity characteristics that are hard to extract and the small particle size of underwater bubbles that are hard to detect. To increase the precision of bubble identification, an image fusion technique based on bubble polarization degree is first presented. Second, we quantitatively investigate the grayscale undulation of bubbles with different size and number distributions in the image from both statistical and experimental aspects, introduce image grayscale fluctuation (GF) to fuse two modes of laser polarization and the image, establish an a posteriori distribution probability model of discriminating features such as the size and number of bubbles, and realize the bubble small-sample, multi-source data fitting. The findings demonstrate that dynamic bubble detection in the 50–1000 μm and 100–2000 cm−3 ranges can achieve more than 95%, as well as more than a 93%, accuracy in quantity distribution and bubble size change. This technique achieves the continuous perception of bubble features in complicated underwater environments, and offers a possible application scheme for the detection of marine bubble environments. [ABSTRACT FROM AUTHOR]

This paper presents a novel integration of Systems Engineering (SE) methodologies and Industry 4.0 (I4.0) technologies in the design of robotic systems, focusing on enhancing underwater robotic missions. Using the conceptual design of an underwater exploration vehicle as a case study, we demonstrate how SE can systematically incorporate I4.0 tools to improve mission performance and meet stakeholder expectations. The study begins with an overview of the SE approach, emphasizing the conceptual design stage and aligning it with the application and case study of design theories. We then explore various I4.0 technologies, highlighting their functional benefits rather than technical specifics and addressing design methods for I4.0. Remotely Operated Vehicles (ROVs) are examined in terms of classification, components, and tasks, showcasing their evolution driven by technological advancements, thus tackling the complexity and design of complex systems. The core of our study involves defining stakeholder expectations, using quality function deployment for requirements definition, and performing a functional and logical decomposition of the ROV system. To deal with design fixation within the design team, we developed a tool to help integrate new technologies by also empathizing with their functional capabilities rather than the technology itself. Our approach underscores the importance of understanding and incorporating new technologies functionally, aligning with the transition towards Industry/Society 5.0. This work not only illustrates the synergy between SE and I4.0, but also offers a structured methodology for advancing the design and functionality of complex systems, setting a blueprint for future developments in this field. [ABSTRACT FROM AUTHOR]

The retracting and releasing cables were the only link between the submarine exploration equipment and the ship, had the functions of power, information transmission and load-bearing, and were widely used in all kinds of launch and recovery systems. The safety and reliability of retracting-releasing cables were of the core functional requirements. Therefore, conducting experiments on mechanics properties of retracting-releasing cables was the foundation of retracting-releasing cable research. Starting from the types and failure modes of ocean winches and retracting-releasing cables, the state of experimental studies of the mechanics properties of retracting-releasing cables was systematically summarized, and an outlook on future research directions was provided. Firstly, the functions of ocean winches and retracting-releasing cables were categorized, followed by a detailed analysis of the forms and reasons for their failures. Secondly, the operating conditions, mechanics performance requirements of retracting-releasing cables, and experimental researchs current status were described. Lastly, the research directions for the mechanics properties of retracting-releasing cables in ocean detection equipment were discussed, including the impact and mechanism of extreme operating environments on mechanics properties of synthetic retracting-releasing cables, the behavior and mechanism of synthetic retracting-releasing cable damage under multiple load couplings, and the technology and integrated simulation devices for retracting-releasing cable mechanics properties. [ABSTRACT FROM AUTHOR]

Soft and lightweight grippers have greatly enhanced the performance of robotic manipulators in handling complex objects with varying shape, texture, and stiffness. However, the combination of universal grasping with passive sensing capabilities still presents challenges. To overcome this limitation, a fluidic soft gripper is introduced based on the buckling of soft, thin hemispherical shells. Leveraging a single fluidic pressure input, the soft gripper can grasp slippery and delicate objects while passively providing information on this physical interaction. Guided by analytical, numerical, and experimental tools, the novel grasping principle of this mechanics‐based soft gripper is explored. First, the buckling behavior of a free hemisphere is characterized as a function of its geometric parameters. Inspired by the free hemisphere's two‐lobe mode shape ideal for grasping purposes, it is demonstrated that the gripper can perform dexterous manipulation and gentle gripping of fragile objects in confined spaces and underwater environments. Last, the soft gripper's embedded capability of detecting contact, grasping, and release conditions during the interaction with an unknown object is proved. This simple buckling‐based soft gripper opens new avenues for the design of adaptive gripper morphologies with tactile sensing capabilities for applications ranging from medical and agricultural robotics to space and underwater exploration. [ABSTRACT FROM AUTHOR]

In the realm of underwater exploration and operations, soft robots exhibit considerable application potential due to their capacity for agile and complex deformations as well as their inherent compliance. These characteristics grant them excellent environmental adaptability and reduce damage to delicate samples and organisms. Nonetheless, existing underwater soft robots are primarily designed to mimic the movements of marine organisms or for specific functions, and little attention is paid to the camouflage ability. To address these challenges, in this study, a reconfigurable transparent soft robot with variable stiffness for underwater operations is presented. The design and fabrication methodology of the robot module is presented, followed by the kinematic analysis and stiffness characterization. Leveraging the proposed robot module, a soft manipulator and a soft gripper are designed for underwater operations. The soft manipulator excels in underwater pipeline detection and obstacle avoidance, while the soft gripper showcases considerable load‐bearing capacity (about 71 times its weight), making it suitable for tasks such as retrieving aquatic biological samples or garbage fishing. The proposed reconfigurable soft robot demonstrates robust camouflage capabilities, high environmental adaptability, and notable versatility, suggesting potential solutions to existing challenges in the field of underwater exploration. [ABSTRACT FROM AUTHOR]

Nowadays, a variety of underwater activities, such as underwater surveillance, marine monitoring, etc., are becoming crucial worldwide. Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted for underwater exploration. Underwater communication via radio frequency (RF) or acoustic wave suffers high transmission loss and limited bandwidth. In this work, we present and demonstrate a rolling shutter (RS)-based underwater optical camera communication (UWOCC) system utilizing a long short-term memory neural network (LSTM-NN) with side glow optical fiber (SGOF). SGOF is made of poly-methyl methacrylate (PMMA) SGOF. It is lightweight and flexibly bendable. Most importantly, SGOF is water resistant; hence, it can be installed in an underwater environment to provide 360° "omni-directional" uniform radial light emission around its circumference. This large FOV can fascinate the optical detection in underwater turbulent environments. The proposed LSTM-NN has the time-memorizing characteristics to enhance UWOCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPB-NN in the UWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-forward error correction (FEC) condition (i.e., bit error rate, BER ≤ 3.8 × 10−3). We also found that thin fiber also allows performing spatial multiplexing to enhance transmission capacity. [ABSTRACT FROM AUTHOR]

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, an innovative enhancement algorithm is proposed, which integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is introduced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. Finally, the image's high and low frequency information are utilized to fuse different features. To demonstrate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach outperforms other mainstream methods, achieving superior PSNR and structural similarity index measure (SSIM) metrics and yielding a detection performance improvement of over ten percent. [ABSTRACT FROM AUTHOR]

Underwater methane (CH4) detection technology is of great significance to the leakage monitoring and location of marine natural gas transportation pipelines, the exploration of submarine hydrothermal activity, and the monitoring of submarine volcanic activity. In order to improve the safety of underwater CH4 detection mission, it is necessary to study the effect of hydrogen sulfide (H2S) in leaking CH4 gas on sensor performance and harmful influence, so as to evaluate the health status and life prediction of underwater CH4 sensor arrays. In the process of detecting CH4, the accuracy decreases when H2S is found in the ocean water. In this study, we proposed an explainable sorted-sparse (ESS) transformer model for concentration interval detection under industrial conditions. The time complexity was decreased to O (n logn) using an explainable sorted-sparse block. Additionally, we proposed the Ocean X generative pre-trained transformer (GPT) model to achieve the online monitoring of the health of the sensors. The ESS transformer model was embedded in the Ocean X GPT model. When the program satisfied the special instructions, it would jump between models, and the online-monitoring question-answering session would be completed. The accuracy of the online monitoring of system health is equal to that of the ESS transformer model. This Ocean-X-generated model can provide a lot of expert information about sensor array failures and electronic noses by text and speech alone. This model had an accuracy of 0.99, which was superior to related models, including transformer encoder (0.98) and convolutional neural networks (CNN) + support vector machine (SVM) (0.97). The Ocean X GPT model for offline question-and-answer tasks had a high mean accuracy (0.99), which was superior to the related models, including long short-term memory–auto encoder (LSTM–AE) (0.96) and GPT decoder (0.98). [ABSTRACT FROM AUTHOR]

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments. [ABSTRACT FROM AUTHOR]

Active and passive sonar are the two types of empirical underwater acoustic channel models (UWACMs). Passive sonar UWACMs have applications in military, ocean exploration, and search and rescue (SAR) activities. However, high transmission loss (TL), multipath propagation, and ambient noise pose significant challenges to signal-to-noise ratio (SNR) and communication effectiveness. To address these challenges, this paper develops a UWACM based on the passive sonar equation method to determine SNR in deep-sea environments, specifically for SAR operations. Determining SNR involves characterizing signal propagation in terms of TL. Existing models lack analysis of TL and SNR for various deep-sea multipath propagation scenarios relevant to SAR applications. Therefore, this paper analyses TL and SNR for both direct and various multipath propagation modes, including surface reflection (SR), surface duct (SD), bottom bounce (BB), convergence zone (CZ), deep sound channel (DSC), and reliable acoustic paths (RAPs) in the deep sea. This work aims to quantify the detection capabilities of underwater location beacons (ULBs) under various deep-sea scenarios and configurations. By analyzing ULB signal propagation characteristics, this research seeks to address key challenges related to ULB performance and ultimately improve SAR operations. The results of the proposed model significantly correlate with existing literature, confirming its accuracy. [ABSTRACT FROM AUTHOR]

The utilization of unmanned sailboats as a burgeoning instrument for ocean exploration and monitoring is steadily rising. In this study, a dual sail configuration is put forth to augment the sailboats' proficiency in its wind-catching ability and adapt to the harsh environment of the sea. This proposition is based on a thorough investigation of sail aerodynamics. The symmetric rigid wing sails NACA 0020 and NACA 0016 are selected for use as the mainsail and trailing wing sail, respectively, after considering the operational environment of unmanned sailboats. The wing sail structure is modeled using SolidWorks, and computational fluid dynamics (CFD) simulations are conducted using ANSYS Fluent 2022R1 software to evaluate the aerodynamic performance of the sails. Key aerodynamic parameters, including lift, drag, lift coefficient, drag coefficient, and thrust coefficient, are obtained under different angles of attack. Furthermore, the effects of mainsail aspect ratios, mainsail taper ratios, and the positional relationship between the mainsail and trailing sail on performance are analyzed to determine the optimal structure. The thrust provided by the sail to the boat is mainly generated by the decomposition of lift, and the lift coefficient is used to measure the efficiency of an object in generating lift in the air. The proposed sail structure demonstrates a 37.1% improvement in the peak lift coefficient compared to traditional flexible sails and exhibits strong propulsion capability, indicating its potential for widespread application in the marine field. [ABSTRACT FROM AUTHOR]

Liquid metal‐elastomer composites have immense potential for emerging electronic applications due to their exceptional mechanical and functional properties. Underwater exploration is one area of interest for these composites, where divers or robots could benefit from wearable sensing, health monitoring, and energy harvesting. However, the influence of water submersion on liquid metal composites is not well understood. Here, the effect of underwater environments on the mechanical, thermal, and electrical properties of liquid metal composites exposed to month‐long aging is quantified. Under most conditions, liquid metal composites maintain their initial properties over the course of aging. However, for composites with high volume loadings of liquid metal (ϕ = 80%) exposed to saltwater conditions, a decrease up to 70% in tensile modulus and up to 44% in thermal conductivity can occur. The aging environments were not shown to have an effect on the electrical conductivity of the composites. Liquid metal composites are then shown to outperform traditional copper electrodes after saltwater aging, demonstrating these soft, multifunctional composites as strong candidates for wearable, water‐resistant devices and machines. [ABSTRACT FROM AUTHOR]

Human exploration of the ocean has never stopped. A large number of sensors are placed in the ocean to establish ocean sensor networks to obtain more information about the marine environment, crustal dynamic changes and so on. With the development of science and technology, Autonomous Underwater Vehicles (AUV) appear and are widely used in marine sensor networks. Due to the complex and changeable ocean environment, the slow speed of traditional and the lack of reasonable path planning and other reasons, AUV waste a lot of time and energy, which cannot efficiently collect information. In this paper, the Unmanned Aerial Underwater Vehicle (UAUV) is introduced into the ocean sensor network. Establish a two-dimensional scene model, find the best water entry point for UAUV to complete the task in the shortest time and minimum power consumption by traversing the search algorithm, and compare the performance of UAUV cross domain mode and underwater mode to collect marine sensor data. Meanwhile, establishing three-dimensional ocean sensor scene model, and using DDQN algorithm to solve the path planning problem of UAUV. The results show that the cross-domain mode of UAUV in the two-dimensional scene model saves 74.7% times and 24.34% energy compared with the traditional underwater mode. In the three-dimensional scene model, the UAUV is trained to the optimal path by the DDQN algorithm, which saves 60.94% of the time and 20.26% of the energy compared to the traditional underwater mode. The results prove the feasibility, stability and efficiency of UAUV introduction into marine sensor network, the effectiveness of DDQN algorithm to solve UAUV path planning problems. [ABSTRACT FROM AUTHOR]

Underwater autonomous operation is becoming increasingly crucial as a means to escape the hazardous high-pressure deep-sea environment. As a result, it is essential for there to be underwater exploration. The development of sophisticated computer vision is the single most significant factor for the success of underwater autonomous operations. In order to improve low-quality photos and compensate for low-light circumstances, preprocessing is used in underwater vision. This allows for clearer pictures to be seen. In this paper, we propose a deep convolutional neural network (DCNN) method for solving the weakly illuminated problem for underwater pictures. This method combines the max-RGB and shade-of-grey approaches to improve underwater visibility and to train the plotting association necessary to obtain the lighting plot. Using this method, we are able to resolve the problematic of weakly illuminated pictures in a way that is efficient. After the photos have been prepared, a deep convolutional neural network (DCNN) approach is developed for detection and classification in the water. Two updated methods are then utilized in order to adapt the architecture of the DCNN to the qualities of underwater vision. The purpose of this investigation is to present a Kalman Filter (KF) method as a solution to the difficulties associated with underwater communication in terms of object tracking and detection. We were able to separate a section of the object by employing a threshold segment and morphological technique. This allowed us to investigate the invariant moment and area properties of the section. Based on the findings, it can be decided that the suggested technique is useful for monitoring underwater targets using DCNN-KF. Furthermore, it displays high resilience, high accuracy, and real-time characteristics. Results from the simulations show that the suggested model DCNN-KF does a better job of localization than the most advanced methods at the time of the study. [ABSTRACT FROM AUTHOR]

Equipment used in underwater sensing and exploration typically relies on cables or batteries for energy supply, resulting in a limited and inconvenient energy supply and marine environmental pollution that hinder the sustainable development of distributed ocean sensing networks. Here, we design a deep-sea differential-pressure triboelectric nanogenerator (DP-TENG) based on a spiral shaft drive using modified polymer materials to harness the hydrostatic pressure gradient energy at varying ocean depths to power underwater equipment. The spiral shaft structure converts a single compression into multiple rotations of the TENG rotor, achieving efficient conversion of differential pressure energy. The multi-pair electrode design enables the DP-TENG to generate a peak current of 61.7 μA, the instantaneous current density can reach 0.69 μA cm-2, and the output performance can be improved by optimizing the spiral angle of the shaft. The DP-TENG can charge a 33 μF capacitor to 17.5 V within five working cycles. It can also power a digital calculator and light up 116 commercial power light-emitting diodes, demonstrating excellent output capability. With its simple structure, low production cost, and small form factor, the DP-TENG can be seamlessly integrated with underwater vehicles. The results hold broad prospects for underwater blue energy harvesting and are expected to contribute to the development of self-powered equipment toward emerging “smart ocean” and blue economy applications. [ABSTRACT FROM AUTHOR]

Discovering new deep hydrothermal vent systems is one of the biggest challenges in ocean exploration. They are a unique window to elucidate the physical, geochemical, and biological processes that occur on the seafloor and are involved in the evolution of life on Earth. In this study, we present a molecular analysis of the microbial composition within the newly discovered hydrothermal vent field, JaichMaa 'ja 'ag, situated in the Southern Pescadero Basin within the Gulf of California. During the cruise expedition FK181031 in 2018, 33 sediment cores were collected from various sites within the Pescadero vent fields and processed for 16S rRNA amplicon sequence variants (ASVs) and geochemical analysis. Correlative analysis of the chemical composition of hydrothermal pore fluids and microbial abundances identified several sediment-associated phyla, including Thermotogota, that appear to be enriched in sediment horizons impacted by hydrothermal fluid flow. Comparative analysis of Thermotogota with the previously explored Auka hydrothermal vent field situated 2 km away displayed broad similarity between the two locations, although at finer scales (e.g., ASV level), there were notable differences that point to core-to-core and site-level factors revealing distinct patterns of distribution and abundance within these two sediment-hosted hydrothermal vent fields. These patterns are intricately linked to the specific physical and geochemical conditions defining each vent, illuminating the complexity of this unique deep ocean chemosynthetic ecosystem. [ABSTRACT FROM AUTHOR]

The powerful eruption of Hunga volcano (15‐January‐2022) excavated ∼6.3 km3 of pre‐existing material, leaving behind an 855 m deep crater. The scientific and humanitarian response to this event was challenging due to the remote location, safety concerns, and COVID‐19 pandemic restrictions. To investigate the status of ongoing eruptive/hydrothermal activity, this study used, for the first time, an un‐crewed surface vessel operated remotely from >16,000 km away to make direct water column measurements within the crater and map its structure in detail. Intense turbidity and oxidation‐reduction potential (ORP) anomalies located ongoing activity at sites on the steep inside crater slopes near both remaining islands. Mid‐water acoustic reflectors indicated ongoing degassing, and positive ORP anomalies suggested gas composition was dominated by CO2. At least 75% of the crater rim is shallower than 100 m, so any exchange with the surrounding ocean is limited by the depths of breaches in the rim (185 m between the islands and 290 m on the ENE side). This post‐eruption bathymetry results in accumulation of emission products within the deep crater. There were no indications of the ongoing activity visible at the ocean surface, which highlights the limitations and inherent biases associated with relying on discolored surface water and/or atmospheric disturbances to determine eruption start/end dates at submarine volcanoes. This study demonstrates the value and need to add repeat hydrothermal plume and bathymetric surveys to our toolbox for monitoring submarine volcanoes, and the potential for un‐crewed, remotely operated vessels to contribute significantly to these efforts. Plain Language Summary: The powerful eruption of Hunga volcano on 15‐January‐2022 sent a plume to space and generated unusual tsunamis. How the eruption impacted the submarine environment was more difficult to determine. We used a highly innovative, un‐crewed vessel equipped with instruments to directly measure characteristics of the water within the 850 m deep crater excavated during the eruption and map the crater's shape in detail. These measurements showed there was ongoing hydrothermal/volcanic activity and CO2 degassing within the crater 7 months after the eruption. The deepest parts of the crater are isolated from the surrounding ocean, so products of this activity become trapped within the crater. While the most powerful eruptions are rare, they can be quite hazardous. The results from this study emphasize the importance of monitoring submarine volcanoes long after signs of eruptions are no longer visible at the ocean surface or atmosphere. Operation of the un‐crewed vessel >16,000 km from the study site was a major technological achievement and the first time that scientists could monitor operations in real time from anywhere around the globe. The success of this mission demonstrated the potential of this innovative technology to contribute to broader applications in ocean exploration, monitoring, and event response. Key Points: Hydrothermal/volcanic activity and CO2 degassing continued at Hunga volcano 7 months after the explosive 15 January 2022 eruptionTurbidity and CO2 are accumulating within the deep crater, which is isolated from the surrounding ocean deeper than ∼200 mFirst use of a novel un‐crewed vessel to conduct over‐the‐horizon bathymetric and water column surveys operated >16,000 km from the study site [ABSTRACT FROM AUTHOR]

High-precision magnetometers play a crucial role in ocean exploration, geophysical prospecting, and military and security applications. Installing them on human-occupied vehicle (HOV) platforms can greatly enhance ocean exploration capabilities and efficiency. However, most existing magnetometers suffer from low sensitivity and excessively large size. This study presents a high-sensitivity, miniaturized magnetometer based on cesium optically pumped probes. The designed magnetometer utilizes a three-probe design to eliminate the detection dead zone of the cesium optically pumped probe and enable three-dimensional magnetic detection. The proposed magnetometer uses a flux gate probe to detect the three-axis magnetic field and ensure that the probe does not enter the dead zone. The three probes can automatically switch by measuring the geomagnetic elements and real-time attitude of the HOV platform. This article primarily introduces the cesium three-probe optically pump, flux gate sensor, and automatic switching scheme design of the above-mentioned magnetometer. Moreover, it is proven through testing that the core indicators, such as the accuracy and sensitivity of the cesium three-probe optically pumped and flux gate sensor, reach international standards. Finally, the effectiveness of the automatic switching scheme proposed in this study is demonstrated through drone-mounted experiments. [ABSTRACT FROM AUTHOR]

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As maritime technology advances, exploration of the oceans has progressively moved from surface exploration to underwater ventures. Unmanned underwater vehicles (UUVs), now prevalent for such exploration, effectively reduce human labor and lower operational costs. These vehicles rely on an internal Battery Storage System (BSS) that sustains device operation by extending operational duration and providing stable voltage. Typically arranged in series, BSSs face challenges due to differences in the chemical characteristics of individual batteries, which lead to discrepancies in battery voltages and cause imbalances during charge and discharge cycles. This results in varied utilization rates among the batteries and uneven aging of the battery pack, potentially decreasing operational efficiency and increasing failure rates, thus reducing reliability and safety. Considering the harsh environmental conditions and maintenance difficulties associated with underwater operations, this paper proposes a robust solution: a balancing system featuring a modular switch with electrical isolation. Through theoretical analysis and circuit simulation, this study constructs and tests a novel prototype of a capacitor-based equalizer circuit with electrical isolation, verifying its feasibility. [ABSTRACT FROM AUTHOR]

Large amounts of video images have been collected for decades by scientific and governmental organizations in deep (>1000 m) water using manned and unmanned submersibles and towed cameras. The collected images were analyzed individually or were mosaiced in small areas with great effort. Here, we provide a workflow for utilizing modern photogrammetry to construct virtual geological outcrops hundreds or thousands of meters in length from these archived video images. The photogrammetry further allows quantitative measurements of these outcrops, which were previously unavailable. Although photogrammetry had been carried out in recent years in the deep sea, it had been limited to small areas with pre-defined overlapping dive paths. Here, we propose a workflow for constructing virtual outcrops from archived exploration dives, which addresses the complicating factors posed by single non-linear and variable-speed vehicle paths. These factors include poor navigation, variable lighting, differential color attenuation due to variable distance from the seafloor, and variable camera orientation with respect to the vehicle. In particular, the lack of accurate navigation necessitates reliance on image quality and the establishment of pseudo-ground-control points to build the photogrammetry model. Our workflow offers an inexpensive method for analyzing deep-sea geological environments from existing video images, particularly when coupled with rock samples. [ABSTRACT FROM AUTHOR]

Summary: The underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate. [ABSTRACT FROM AUTHOR]

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats. [ABSTRACT FROM AUTHOR]

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model's focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model's accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy ( m A P 0.5 ) and ( m A P 0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images. [ABSTRACT FROM AUTHOR]

Rare earth elements (REE) are vital for powerful permanent magnets used in electric motors and wind turbines. These elements are chiefly sourced from carbonatites and their weathering products. The economic attractiveness of carbonatites is explained by the 10,000‐fold enrichment of REE in their mineralized portions relative to the average continental crust. Carbonatites form from mantle‐derived melts, but the ultimate origin of their REE is not completely clear. One widely cited model invokes subduction of marine sediments which accumulate REE‐rich material, priming the mantle to produce REE‐rich carbonatite melts which subsequently form deposits in the upper crust. Here we examine a global marine sediment compilation, revealing a wide variety in REE abundances and patterns. We use the sensitive lambda method that separates REE pattern curvature from redox‐related element anomalies to examine both marine sediments and presumably derived carbonatite rocks. We find that the most REE‐rich marine sediments are characterized by strongly negative Ce anomalies, which if recycled via subduction, mineralized carbonatites are expected to inherit. In contrast, we find that mineralized carbonatite rocks do not contain Ce anomalies. This indicates that the REE from the most REE‐rich marine sediments are not recycled into carbonatite deposits, and a different REE source is needed to explain carbonatite fertilities. We also find evidence that raises questions on whether any sediment‐derived REE are present in carbonatite deposits to a significant amount. We suggest that a REE‐rich source may not be required and REE enrichment occurs primarily during crustal magmatic differentiation. Plain Language Summary: Rare earth elements (REE) such as neodymium are important components of permanent magnets used in energy generation technologies including wind power turbines and electric motors. These metals are extracted from carbonatite rocks, but how the REE got to carbonatites in the first place is contentious. The seafloor contains accumulations of exceptionally REE‐rich sediments, and a popular model proposes that these REE are recycled to the mantle during subduction. Once melting occurs in the mantle, these same REE are suggested to be transported in the carbonatite melts and eventually reach the upper crust where they contribute to economic mineralization. However, the REE‐rich sediments contain a ubiquitous chemical signature in the form of a redox‐sensitive negative cerium anomaly. We show that the anomaly does not occur in carbonatite rocks, with the implication that recycled REE‐rich sediments cannot necessarily be the source for carbonatite‐related ore deposits. Key Points: Subducted REE‐rich sediments with negative Ce anomalies are thought to supply carbonatites with their REECarbonatites have no statistically significant negative Ce anomalyRecycling of particularly REE‐rich marine sediments not required for mineralization [ABSTRACT FROM AUTHOR]

The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities. [ABSTRACT FROM AUTHOR]

With the intensification of ocean exploration and development in recent years, the navigation equipment in the marine environment has become increasingly diversified, making the marine environment more complex. Traditional methods for underwater target recognition are gradually becoming less applicable and unable to achieve better results. With the application of deep learning in underwater target recognition, the robustness of deep learning models used for underwater target recognition is crucial due to the significant environmental interference in underwater target data and the susceptibility of deep learning models to adversarial samples. This paper proposes an error sample feature compensation method for improving the robustness of deep learning models for underwater target recognition, focusing on the problem of the significant impact of sample data quality on the robustness of deep learning models for underwater target recognition. The method innovatively divides error samples into difficult-to-improve samples and easy-to-improve samples and proposes an adversarial training method combined with classification conditions. At the same time, the method uses a weighted index of model accuracy to combine adversarial training models with feature compensation methods, further improving the robustness of deep learning models for underwater target recognition tasks. Finally, the method is validated on an underwater dataset, and the results show that the proposed method improves the robustness of deep learning models used for underwater target recognition. [ABSTRACT FROM AUTHOR]

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration. [ABSTRACT FROM AUTHOR]

Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancement. More specifically, our framework includes a light field module (LFM) and sketch module, aiming at the generation of a light field map of the target image for improving the color representation and preserving the details of the original image by providing contour information. The restored underwater image is gradually enhanced, guided by the light field map. The experimental results show the better image restoration effectiveness, both quantitatively and qualitatively, of the proposed method with a lower (or comparable) computing cost, compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

As a key method of ocean exploration, the positioning accuracy of autonomous underwater vehicles (AUVs) directly influences the success of subsequent missions. This study aims to develop a novel method to address the low accuracy in visual simultaneous localization and mapping (SLAM) within underwater environments, enhancing its application in the navigation and localization of AUVs. We propose an underwater multisensor fusion SLAM system based on image enhancement. First, we integrate hybrid attention mechanisms with generative adversarial networks to address the blurring and low contrast in underwater images, thereby increasing the number of feature points. Next, we develop an underwater feature-matching algorithm based on a local matcher to solve the feature tracking problem caused by grayscale changes in the enhanced image. Finally, we tightly couple the Doppler velocity log (DVL) with the SLAM algorithm to better adapt to underwater environments. The experiments demonstrate that, compared to other algorithms, our proposed method achieves reductions in both mean absolute error (MAE) and standard deviation (STD) by up to 68.18% and 44.44%, respectively, when all algorithms are operating normally. Additionally, the MAE and STD of our algorithm are 0.84 m and 0.48 m, respectively, when other algorithms fail to operate properly. [ABSTRACT FROM AUTHOR]

Driven by the demands of ocean exploration, an increasing number of manta ray-inspired robots have been designed and manufactured, primarily utilizing flexible skeletons combined with motor-driven mechanisms. However, the mechanical analysis of these designs remains underdeveloped, often relying on simplistic imitation of biological prototypes and typically neglecting the vertical motion induced by pectoral fin flapping. This paper presents a fluid–structure interaction analysis framework that couples rigid body motion with elastic deformation using flexible multibody dynamics and the vortex particle method. An implicit iterative algorithm with Aitken relaxation is employed to address added-mass instability, and the framework has been validated against experimental data. An analysis of a representative manta-bot model shows that self-induced vertical undulations reduce the thrust coefficient by approximately 40% compared to fixed vertical degrees of freedom, while slightly improving overall propulsive efficiency. The study also highlights the critical role of mass distribution in manta-bots, noting that excessive focus on complex pectoral fin movements and large fin mass can significantly reduce thrust by increasing vertical displacement, ultimately proving counterproductive. [ABSTRACT FROM AUTHOR]

Underwater optics have seen a notable surge of interest in recent years, emerging as a critical medium for conveying information crucial to underwater resource exploration, autonomous underwater vehicle navigation, etc. The intricate dynamics of underwater optical transmission, influenced by factors such as the absorption by the water and scattering by multiple particles, present considerable challenges. One of the most critical issues is that the optical information representation methods fail to take into account the impact of the underwater physical environment. We conducted a comprehensive review and analysis of recent advancements in underwater optical transmission laws and models. We summarized and analyzed relevant research on the effects of underwater particles and turbulence on light and analyzed the polarization effects in various environments. Then, the roles of various types of underwater optical propagation models were analyzed. Although optical models in complex environments are still mostly based on Monte Carlo methods, many underwater optical propagation mechanisms have been revealed and can promote the impacts of optical information expression. We delved into the cutting-edge research findings across three key domains: the enhancement of underwater optical image quality, the 3D reconstruction from monocular images, and the underwater wireless optical communication, examining the pivotal role played by light transmission laws and models in these areas. Drawing upon our extensive experience in underwater optics, including underwater optical sensor development and experiments, we identified and underscored future directions in this field. We advocate for the necessity of further advancements in the comprehension of underwater optical laws and physical models, emphasizing the importance of their expanded application in underwater optical information representations. Deeper exploration into these areas is not only warranted but essential for pushing the boundaries of current underwater optical technologies and unlocking new potential for their application in underwater optical sensor developments, underwater exploration, environmental monitoring, and beyond. [ABSTRACT FROM AUTHOR]