Ocean Exploration Research Articles

Fluid–Structure Interaction Analysis of Manta-Bots with Self-Induced Vertical Undulations during Fin-Based Locomotion

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.

AUV path planning in a three-dimensional marine environment based on a novel multiple swarm co-evolutionary algorithm

Autonomous underwater vehicles (AUVs) are rapidly advancing in ocean exploration. High-performance path planning techniques are essential for AUVs. Path planning for AUVs is a multi-faceted challenge, necessitating careful consideration of safety, energy consumption and the influence of sea currents to ensure the development of a high-quality trajectory that satisfies all operational criteria. Existing path planning algorithms have problems such as incomplete consideration of influencing factors, computational complexity and weak applicability. Therefore, we propose a swarm intelligence optimisation algorithm based on multiple swarm co-evolution (MCO) to address the issue of AUV path planning in a three-dimensional marine environment. First, a three-dimensional marine environment model and the corresponding path evaluation mechanism are established. Second, the MCO rule is established to ensure that the MCO has balanced exploration and exploitation capabilities. A shared dynamic optimal particle between populations is introduced to ensure information exchange between populations. In addition, the cross-integration mutation strategy has been proposed for promoting the fusion of two populations of superior genes to ensure the inheritance of superior paternal genes to the offspring. Finally, four comparison experiments are designed, and the experiments compare eight commonly used intelligent search algorithms and improved versions. The results of the experiments proved that the MCO has excellent three-dimensional marine environment path planning capability, with robustness and search capabilities superior to other swarm intelligence optimisation algorithms.

Integrating Seabed Topography and Ocean Current Dynamics for Submarine Trajectory Analysis

In the face of growing global ocean exploration, the research focuses on overcoming the challenges of deep-sea navigation and the unpredictability of ocean currents to improve submarine pathway planning and trajectory prediction. This study proposes a method for improving submarine navigation and trajectory prediction through digital seabed mapping of the Ionian Sea and ocean current simulation. Using NCEI data, it transforms latitude and longitude into a geographic model, bypassing seawater variables like salinity and temperature. The approach combines sine functions and Gaussian noise to mimic ocean currents' natural variability, enhancing position predictions. Simplifying the submarine as a sphere, it integrates ocean currents into motion analysis and employs MATLAB for simulation, including scenarios of contact loss and breakup. Recognizing model limitations, it recommends using sensors on submarines for real-time data collection, aiming to refine navigational accuracy amidst environmental uncertainties.

What Causes Excess Deepening of the Sediment Mixed Layer in the Deep Ocean?

AbstractThe sediment mixed layer (SML) in the deep ocean is an important interface with a rich diversity of benthic organisms. With increasing ocean mineral exploration, and eventual mining, the effect of sediment mixing on deep ocean ecosystems has raised considerable concern. We evaluate the distribution patterns and driving factors of SML depth in deep ocean nodule fields using naturally occurring 210Pb–226Ra isotopes. Results show that average SML depth has increased in Mn‐nodule fields since the end of the last century. SML processes are associated with significant desorption of 226Ra from sediments, resulting in a departure from radioactive equilibrium. By estimating possible driving factors, we conclude that anthropogenic exploration activities, rather than natural physical and/or biological drivers, are the most likely mechanism for intensified sediment mixing. 210Pb–226Ra disequilibria may be a potential tracer for quantifying the impact of human exploration on deep‐ocean sediment mixing and associated biological and geochemical effects.

Research and Implementation of Pneumatic Amphibious Soft Bionic Robot

To meet the requirements of amphibious exploration, ocean exploration, and military reconnaissance tasks, a pneumatic amphibious soft bionic robot was developed by taking advantage of the structural characteristics, motion forms, and propulsion mechanisms of the sea lion fore-flippers, inchworms, Carangidae tails, and dolphin tails. Using silicone rubber as the main material of the robot, combined with the driving mechanism of the pneumatic soft bionic actuator, and based on the theory of mechanism design, a systematic structural design of the pneumatic amphibious soft bionic robot was carried out from the aspects of flippers, tail, head–neck, and trunk. Then, a numerical simulation algorithm was used to analyze the main executing mechanisms and their coordinated motion performance of the soft bionic robot and to verify the rationality and feasibility of the robot structure design and motion forms. With the use of rapid prototyping technology to complete the construction of the robot prototype body, based on the motion amplitude, frequency, and phase of the bionic prototype, the main execution mechanisms of the robot were controlled through a pneumatic system to carry out experimental testing. The results show that the performance of the robot is consistent with the original design and numerical simulation predictions, and it can achieve certain maneuverability, flexibility, and environmental adaptability. The significance of this work is the development of a pneumatic soft bionic robot suitable for amphibious environments, which provides a new idea for the bionic design and application of pneumatic soft robots.

State Policy of Far East Development: How to Ensure Accelerated Progress?

Against the backdrop of the authorities’ efforts to accelerate the development of the Far East, dissatisfaction remains, an indicator of which is the trend of population decline. The article argues that the growing financial flow, with the same approach, without a precise formula of the goal, without the definition of priorities, will not give the desired result. It is proposed to accelerate the transition from a resource-export model of the economy to a new industrialization. The justification of priorities is based on combining the efforts of the subjects of the federation of the macroregion, involving the potential of the country’s entrepreneurs focused on Asian markets, and integration cooperation with the countries of the AsiaPacific region.The widest field for cooperation is seen in the field of exploration of the World Ocean, space exploration and in the field of Culture. Such a turn increases the attractiveness of the Far East and increases the demand for labor. It will be necessary to see a different solution to demographic problems. If the State is the main beneficiary of population growth, it is obliged to recognize the birth and upbringing of children in the family as productive work, which is remunerated, counted in the length of service, and provided with a pension.The stability of a young family is determined by the availability of housing, and the key to having many children is a house “on the ground”. But the house is not financial bondage, not a mortgage, but an incentive for expanded reproduction. The course towards industrialization provides an opportunity for a decent income for the head of the family, the work of women is paid, and a roof over their heads is a guarantee of a stable and large family. And the Far East is a testing ground where a model is created, control mechanisms are polished, and efficiency is demonstrated.

MODELING AND OPTIMIZATION OF A PCM-BASED OCEAN THERMAL ENERGY HARVESTER FOR POWERING UNCREWED UNDERWATER VEHICLES

Abstract As oceans cover over 70% of the planet's surface, they represent a large reservoir of resources that remained vastly untapped. Uncrewed Underwater Vehicles (UUVs) are becoming key technology for ocean exploration. Ocean thermal gradient is a permanent and reliable energy source that can be used to power UUVs using phase change material (PCM)-based thermal engines. When using PCM-based thermal engines to power UUVs, there are different energy conversion stages, thermal, hydraulic, kinetic, and electrical, dependent on a wide variety of parameters. Thus, optimization of the overall energy conversion is still a challenge for powering the increasing energy demanding UUVs for long missions. The goal of this study is to propose a PCM-based ocean thermal energy harvesting system for powering float type UUVs such as the Solo II float. This reduces the cost for battery replacement and expands the float's mission time. For this purpose, we developed a model consisting of hydraulic and electrical systems, designed to provide the electrical power needed by the UUV. The hydraulic and electrical systems are implemented using MATLAB-Simulink. The model developed can provide 13.66 kJ of electrical energy, which is more than 1.5 times the energy requirement per cycle for the SOLO II float.

Investigation of the Impact Load Characteristics during Water Entry of Airdropped Underwater Gliders

Underwater gliders have emerged as effective tools for long-term ocean exploration. Employing aircraft for launching underwater gliders could significantly expand their application. Compared to slender underwater vehicles, the distinctive wing structure of underwater gliders may endure huge impact forces when entering water, leading to more intricate impact load characteristics and potential wing damage. This paper employs a computational fluid dynamics approach to analyze the water entry event of an airdropped underwater glider and its impact load behavior. The results indicate that the glider impact load is enhanced prominently by the wing, and that the extent of enhancement is influenced by the entry attitude. At an entry angle of 80°, the glider exhibits the maximum impact load during different water entry angles. In addition, a larger attack angle indicates a higher glider impact load. Our present study holds significant importance for both the hydrodynamic shape design and water entry strategy control of airdropped underwater gliders.

Research on ultrasonic-electromagnetic wave simultaneous sensing sensors

Ultrasonic and electromagnetic wave detection signals are effective characterization signals for state detection, such as medical imaging, ocean exploration, and industrial applications.In order to simplify the complexity of ultrasonic and electromagnetic wave signal detection, this paper proposes a sensor design idea with both ultrasonic and electromagnetic wave sensing functions, and develops an ultrasonic-electromagnetic wave simultaneous sensing sensor based on the application scenario of ultrasonic and electromagnetic wave signal sensing requirements of discharge insulation defects of power equipment.The simulation results show that the voltage standing wave ratio of the sensor designed in this paper is less than 3.3 in the electromagnetic wave band of 0.3 GHz ∼ 3.0 GHz. The average ultrasonic sensitivity in the range of 20 kHz ∼ 80 kHz is 48 dB ( Ref 1 V / ( m / s)), and the peak sensitivity is 62 dB.Finally, the construction of power equipment discharge insulation defects simulation experimental platform, ultrasonic - electromagnetic wave simultaneous sensing sensor performance testing, the experimental results show that the sensor can be developed at the same time accurately detect the ultrasonic and electromagnetic wave signals, the results of the research in this paper will significantly reduce the application cost of ultrasonic and electromagnetic wave detection device.

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

SummaryThe 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.

U-ATSS: A lightweight and accurate one-stage underwater object detection network

Due to the harsh and unknown marine environment and the limited diving ability of human beings, underwater robots become an important role in ocean exploration and development. However, the performance of underwater robots is limited by blurred images, low contrast and color deviation, which are resulted from complex underwater imaging environments. The existing mainstream object detection networks perform poorly when applied directly to underwater tasks. Although using a cascaded detector network can get high accuracy, the inference speed is too slow to apply to actual tasks. To address the above problems, this paper proposes a lightweight and accurate one-stage underwater object detection network, called U-ATSS. Firstly, we compressed the backbone of ATSS to significantly reduce the number of network parameters and improve the inference speed without losing the detection accuracy, to achieve lightweight and real-time performance of the underwater object detection network. Then, we propose a plug-and-play receptive field module F-ASPP, which can obtain larger receptive fields and richer spatial information, and optimize the learning rate strategy as well as classification loss function to significantly improve the detection accuracy and convergence speed. We evaluated and compared U-ATSS with other methods on the Kesci Underwater Object Detection Algorithm Competition dataset containing a variety of marine organisms. The experimental results show that U-ATSS not only has obvious lightweight characteristics, but also shows excellent performance and competitiveness in terms of detection accuracy.

Scale effects on the hydrodynamic performance of a submerged body

The increasing development and utilization of submerged bodies in ocean exploration have led to significant challenges due to their unique working environment. Conducting experiments with a full-scale submerged body is exceptionally difficult. To investigate scale effects on the hydrodynamic characteristics of submerged bodies, a three-dimensional numerical model for submerged bodies was established based on the Reynolds-averaged Navier–Stokes (RANS) equations and utilizing the Realizable k-ɛ turbulence model. The volume of fluid (VOF) method was used for interface capture. Initially, the reliability of the numerical model was verified through result validation and convergence analysis of the grid and time steps. Subsequently, SUBOFF model of different scales were adopted using the established numerical model considering the influence of the Froude number (Fr). The present study focused on investigating the effect of model scales on the wave pattern, resistance characteristics, and nominal wake fields. Research has shown that the scale effects on the frictional resistance and the wake field of the submerged body are significant. The numerical model used in the present study demonstrated good accuracy. A comprehensive analysis of the scale effects on submerged bodies was conducted, providing valuable insights for the application of model data to full-scale scenarios.

Autonomous Underwater Self-Healable Adhesive Elastomers Enabled by Dynamical Hydrophobic Phase-Separated Microdomains.

High-efficient underwater self-healing materials with reliable mechanical attributes hold great promise for applications in ocean explorations and diverse underwater operations. Nevertheless, achieving these functions in aquatic environments is challenging because the recombination of dynamic interactions will suffer from resistance to interfacial water molecules. Herein, an ultra-robust and all-environment stable self-healable polyurethane-amide supramolecular elastomer is developed through rational engineering of hydrophobic domains and multistrength hydrogen bonding interactions to provide mechanical and healing compatibility as well as efficient suppression of water ingress. The coupling of hydrophobic chains and hierarchical hydrogen bonds within a multiphase matrix self-assemble to generate dynamical hydrophobic hard-phase microdomains, which synergistically realize high stretchability (1601%), extreme toughness (87.1MJm-3), and outstanding capability to autonomous self-healing in various harsh aqueous conditions with an efficiency of 58% and healed strength of 12.7MPa underwater. Furthermore, the self-aggregation of hydrophobic clusters with sufficient dynamic interactions endows the resultant elastomer with effective instantaneous adhesion (6.2MPa, 941.9Nm-1) in extremely harsh aqueous conditions. It is revealed that the dynamical hydrophobic hard-phase microdomain composed of hydrophobic barriers and cooperative reversible interactions allows for regulating its mechanical enhancement and underwater self-healing efficiency, enabling the elastomers as intelligent sealing devices in marine applications.

A hovering micro autonomous underwater vehicle with integrated control and positioning system

Autonomous underwater vehicle (AUV) is currently the mainstream equipment for ocean exploration due to its long endurance and large operating range. However, the torpedo profile of traditional AUVs limits their maneuverability and hovering capabilities. This hinders the application and popularity of AUVs in near-seabed operations and accurate detection in small areas. This paper presents a novel disc-shaped micro AUV characterized by using a multi-core heterogeneous architecture to integrate control and positioning systems to obtain a lightweight design with complete functionality. Through the special structure and adaptive sliding mode controller, it can achieve high maneuvering navigation and stable hovering under different working conditions. A passive inverted ultra-short baseline (piUSBL) underwater positioning system is developed to provide accurate and instantaneous underwater position at low cost and power consumption. Field experiments, including special circular cruising and acoustic docking, were conducted in an open-water environment. The experimental results show that this vehicle has a depth control accuracy of 5 cm and a heading control accuracy of 3°, which well proves that this prototype can carry out accurate detection in a small range. In addition, this vehicle has a diameter of only 42 cm, a height of 23 cm, and a weight of 14 kg, making it easy to deploy and recover. To the best of our knowledge, this is the first AUV to be successfully tested in the field using a multi-core heterogeneous architecture with such small size and light weight.

Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies.

A novel morphing nose cone for underwater gliders: Performance analysis, parameter optimization, and driving mechanism design

As an autonomous underwater vehicle with low energy consumption, underwater glider has been widely applied to long-term ocean exploration missions. To better deal with complex working environments and various mission requirements, morphing underwater gliders are receiving increasing attention. This paper attempts to introduce the morphing nose cone that can achieve length and bending angle adjustments into the glider to improve its comprehensive performance. The performance indicators of the glider include energy utilization rate, static stability, and voyage velocity. Based on dynamic analysis and CFD simulation, performance evaluation models of a glider are established, and the effect of morphing nose cone configurations on the glider's hydrodynamic characteristics is discussed. Then, mathematical optimization models for morphing parameters of the nose cone are established, and they are solved by a multi-objective optimization algorithm and surrogate model technology. Especially, the diving and ascending motion processes of the glider are separately considered in the above optimization. Finally, the multi-objective optimization results are used for guiding the driving mechanism design of the morphing nose cone, and the principle prototype of a novel design scheme is manufactured to verify the feasibility. The research methods and results will provide new development direction for the morphing underwater glider technology.

A Highly Sensitive Deep-Sea Hydrodynamic Pressure Sensor Inspired by Fish Lateral Line.

Hydrodynamic pressure sensors offer an auxiliary approach for ocean exploration by unmanned underwater vehicles (UUVs). However, existing hydrodynamic pressure sensors often lack the ability to monitor subtle hydrodynamic stimuli in deep-sea environments. In this study, we present the development of a deep-sea hydrodynamic pressure sensor (DSHPS) capable of operating over a wide range of water depths while maintaining exceptional hydrodynamic sensing performance. The DSHPS device was systematically optimized by considering factors such as piezoelectric polyvinylidene fluoride-trifluoroethylene/barium titanate [P(VDF-TrFE)/BTO] nanofibers, electrode configurations, sensing element dimensions, integrated circuits, and packaging strategies. The optimized DSHPS exhibited a remarkable pressure gradient response, achieving a minimum pressure difference detection capability of approximately 0.11 Pa. Additionally, the DSHPS demonstrated outstanding performance in the spatial positioning of dipole sources, which was elucidated through theoretical charge modeling and fluid-structure interaction (FSI) simulations. Furthermore, the integration of a high Young's modulus packaging strategy inspired by fish skull morphology ensured reliable sensing capabilities of the DSHPS even at depths of 1000 m in the deep sea. The DSHPS also exhibited consistent and reproducible positioning performance for subtle hydrodynamic stimulus sources across this wide range of water depths. We envision that the development of the DSHPS not only enhances our understanding of the evolutionary aspects of deep-sea canal lateral lines but also paves the way for the advancement of artificial hydrodynamic pressure sensors.

A Global Seawater Density Distribution Model Using a Convolutional Neural Network.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.

Stretchable, stable, and structural color reversible enabled by mesoscopic regulation and design for flexible photonic crystal mechanical sensors

This paper reports a flexible mechanical sensor developed by combining an opal photonic crystal and a flexible medium through UV curing technology. Under the action of external force, the deformation of the flexible medium causes the change of the photonic crystal lattice constant, which leads to the displacement of the reflection peaks, thus realizing the detection of the deformation of the film. The flexible medium is mixed by photoinitiators and polymer monomers, and its mechanical properties are directly related to the mass fraction of photoinitiators and polymer monomers. The results show that the film can reach a maximum strain of about 180% when the mass fraction of photoinitiator is 3% and the volume ratio of the polymer functional monomers is 8:2. In addition, for the responsiveness test of the sensor under tensile, the results show that at 0%, 26%, and 78% of tensile strain, its structural color is blue-shifted sequentially, and its spectral peaks are 668 nm, 535 nm, and 488 nm, respectively. Finally, the sensor is applied to the detection of acoustic waves in air, and it is found that its spectral response peaks can be obtained for acoustic waves of different frequencies (250–650 Hz), and a spectral peak at 500 Hz. This flexible photon sensor has the potential to match the acoustic resistance of water. It will greatly expand the response research of ocean exploration in the low frequency range.

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.