Search Results (20)

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

In the domain of ocean engineering, the authenticity of visual systems is a major challenge in developing marine simulators. A simulation strategy based on the smoothed particle hydrodynamics (SPH) simulation method is proposed in this study to enhance the realism of fluid–solid coupling scenes in a marine simulator visual system. Based on the SPH method, the water particles are constrained in each iteration according to the two physical fields of velocity divergence and density by setting an intermediate velocity. In the simulation of the fluid–structure interaction scenario, the contribution of the volume of the rigid model to the water particles is represented by a spatial map and then incorporated into the calculation of the pressure from the water particles according to the positional relationship between the water particles and the boundary. This strategy can effectively ensure the realism of the interaction between the rigid body and the waves on the one hand and significantly improve the speed of the marine simulator visual system on the other. The experiments show that this strategy can effectively save a significant amount of time and provide theoretical and technical references for enhancing the realism of a marine simulator visual system. Full article

Caused by nonlinear vibration, ocean white noise exhibits complex dynamic characteristics and nonlinear perception characteristics. To explore the potential application of ocean white noise in engineering and health fields, novel methods based on deep learning algorithms are proposed to generate ocean white noise, contributing to marine environment simulation in ocean engineering. A comparative study, including spectrum analysis and auditory testing, proved the superiority of the generation method using deep learning networks over general mathematical or physical methods. To further study the nonlinear perception characteristics of ocean white noise, novel experimental research based on multi-modal perception research methods was carried out within a constructed multi-modal perception system environment, including the following two experiments. The first audiovisual comparative experiment thoroughly explores the system’s user multi-modal perception experience and influence factors, explicitly focusing on the impact of ocean white noise on human perception. The second sound intensity testing experiment is conducted to further explore human multi-sensory interaction and change patterns under white noise stimulation. The experimental results indicate that user visual perception ability and state reach a relatively high level when the sound intensity is close to 50 dB. Further numerical analysis based on the experimental results reveals the internal influence relationship between user perception of multiple senses, showing a fluctuating influence law to user visual concentration and a curvilinear influence law to user visual psychology from the sound intensity of ocean white noise. This study underscores ocean white noise’s positive effect on human perception enhancement and concentration improvement, providing a research basis for multiple field applications such as spiritual healing, perceptual learning, and artistic creation for human beings. Importantly, it provides valuable references and practical insights for professionals in related fields, contributing to the development and utilization of the marine environment. Full article

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period. Full article

Remotely operated vehicles (ROVs) provide practical solutions for a wide range of activities in a particularly challenging domain, despite their dependence on support ships and operators. Recent advancements in AI, machine learning, predictive analytics, control theories, and sensor technologies offer opportunities to make ROVs (semi) autonomous in their operations and to remotely test and monitor their dynamics. This study moves towards that goal by formulating a complete navigation, guidance, and control (NGC) system for a six DoF BlueROV2, offering a solution to the current challenges in the field of marine robotics, particularly in the areas of power supply, communication, stability, operational autonomy, localization, and trajectory planning. The vehicle can operate (semi) autonomously, relying on a sensor acoustic USBL localization system, tethered communication with the surface vessel for power, and a line of sight (LOS) guidance system. This strategy transforms the path control problem into a heading control problem, aligning the vehicle’s movement with a dynamically calculated reference point along the desired path. The control system uses PID controllers implemented in the navigator flight controller board. Additionally, an infrastructure has been developed that synchronizes and communicates between the real ROV and its digital twin within the Unity environment. The digital twin acts as a visual representation of the ROV’s movements and considers hydrodynamic behaviors. This approach combines the physical properties of the ROV with the advanced simulation and analysis capabilities of its digital counterpart. All findings were validated at the Point Rouge port located in Marseille and at the port of Ancona. The NGC implemented has proven positive vehicle stability and trajectory tracking in time despite external interferences. Additionally, the digital part has proven to be a reliable infrastructure for a future bidirectional communication system. Full article

This study is dedicated to the development of an advanced ship piping network programming tool to address the challenges faced by traditional text-based design and computation methods when dealing with complex and large-data-volume piping systems, such as burdensome programming tasks, high error rates, and difficulty in troubleshooting faults. Leveraging Microsoft’s WPF technology and the C# language, combined with Excel as a data input platform, this tool provides an intuitive graphical user interface, allowing users to intuitively build and analyze ship piping network models by dragging and dropping controls. The tool not only simplifies the design process of complex piping systems but also significantly improves efficiency and accuracy through automated data processing and calculations. It supports user customization of key pipeline characteristics, such as maximum flow and direction, further enhancing the applicability and accuracy of the piping network model. In addition, with optimized interaction design and data management methods, the tool significantly reduces the learning difficulty for users, while improving the reliability of design and efficiency of troubleshooting. The results of this study show the tool not only technically outperforms traditional methods but also provides a new efficient, intuitive, and user-friendly tool for the teaching and engineering applications of ship piping networks, paving a new path for the design and optimization of ship piping network systems, with significant practical application value and theoretical significance. Looking forward, this tool is expected to play a broader role in the instruction and industrial practices associated with ship piping networks, moving the field toward more efficient and intelligent development. Full article

Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, such as poor tracking and detection. To solve these problems, we propose a long-term target anti-interference tracking (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks and online learning ideas. Meanwhile, we propose using the cubature Kalman filter (CKF) for optimization and prediction of the position. We constructed a launch and recovery system (LARS) tracking and capturing the AUV. The system consists of the following parts: First, images are acquired via binocular cameras. Next, the relative position between the AUV and the end of the LARS was estimated based on the pixel positions of the tracking AUV feature points and binocular camera data. Finally, using a discrete proportion integration differentiation (PID) method, the LARS is controlled to capture the moving AUV via a CKF-optimized position. To verify the feasibility of our proposed system, we used the robot operating system (ROS) platform and Gazebo software to simulate the system for experiments and visualization. The experiment demonstrates that in the tracking process when the AUV makes a sinusoidal motion with an amplitude of 0.2 m in the three-dimensional space and the relative distance between the AUV and LARS is no more than 1 m, the estimated position error of the AUV does not exceed 0.03 m. In the capturing process, the final capturing error is about 28 mm. Our results verify that our proposed system has high robustness and accuracy, providing the foundation for future AUV recycling research. Full article

A novel fault diagnosis scheme was developed to address the difficulty of feature extraction for planetary gearboxes using refined composite multiscale fluctuation dispersion entropy (RCMFDE) and supervised manifold mapping. The RCMFDE was first utilized in this scheme to fully mine fault features from planetary gearbox signals under multiple scales. Subsequently, as a supervised manifold mapping method, supervised isometric mapping (S-Iso) was applied to decrease the dimensions of the original features and remove redundant information. Lastly, the marine predator algorithm-based support vector machine (MPA-SVM) classifier was employed to achieve intelligent fault diagnosis of planetary gearboxes. The suggested RCMFDE combines the composite coarse-grained construction and refined computing technology, overcoming unstable and invalid entropy in the traditional multiscale fluctuation dispersion entropy. Simulation experiments and fault diagnosis experiments from a real planetary gearbox drive system show that the complexity measure capability and feature extraction effectiveness of the proposed RCMFDE outperform the multiscale fluctuation dispersion entropy (MFDE) and multi-scale permutation entropy (MPE). The S-Iso’s visualization results and dimensionality reduction performance are better than principal components analysis (PCA), linear discriminant analysis (LDA), and isometric mapping (Isomap). Moreover, the suggested fault diagnosis scheme has an accuracy rate of 100% in identifying bearing and gear defects in planetary gearboxes. Full article

For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic identification system data. The density map was statistically created by logarithmically transforming the density values and adopting standard deviation-based stretch visualization to increase the normality of the distribution. Many types of traffic routes such as open-sea, coastal, inland, and coastal access routes were successfully identified; moreover, the stretch color ramp ratio was reduced to identify routes having relatively high density. Adopting a single standard deviation and demonstrating the top 25% of color ramps, the analysis afforded the main routes through which customary traffic flows. This novel density analysis method and statistical visualization method is expected to be used for developing national maritime traffic routes and should ultimately contribute to maritime safety. Moreover, it provides a scientific means and simulator for determining the navigation area and analyzing conflicts with other activities in marine spatial planning. Full article

With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of the reality of ocean data visualization results and the real-time interaction in the visualization process. Aiming at the challenges of 3D interactive key technology and GPU-based visualization algorithm technology, we developed a visualization system for large-scale 3D marine environmental data. The system realizes submarine terrain rendering, contour line visualization, isosurface visualization, section visualization, volume visualization and flow field visualization. In order to manage and express the data in the system, we developed a data management module, which can effectively integrate a large number of marine environmental data and manage them effectively. We developed a series of data analysis functions for the system, such as point query and line query, local analysis and multi-screen collaboration, etc. These functions can effectively improve the data analysis efficiency of users and meet the data analysis needs in multiple scenarios. The marine environmental data visualization system developed in this paper can efficiently and intuitively simulate and display the nature and changing process of marine water environmental factors. Full article

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided. Full article

Crew fatigue from standing posture motion, caused by ship motion, can lead to marine accidents. Therefore, the mechanism of fatigue in crew members ought to be elucidated. The standing posture of humans is maintained by postural state detection through the visual, vestibular, and somatosensory systems. Humans can adjust their posture through corrective postural reactions (CPR) generated after anticipatory postural adjustments (APAs) by using information from these sensory systems. APAs refer to skills acquired by learning from past motions and perturbations and are prepared by the central nervous system based on visual information before the actual perturbation occurs. We hypothesized that APAs would decrease fatigue in crew members by stabilizing their standing posture motions. We aimed to clarify the human standing posture control influenced by APAs based on visual information. To this end, we presented wave images with different wave directions to the participants using a visual simulator and analyzed their standing posture motion. We found that the participants stabilized their standing posture based on the projected wave directions. This showed that the participants predicted ship motion from the wave images and controlled their center of pressure (COP) through APAs. Individual differences in standing postural motion may indicate the subjective variation of APAs based on individual experiences. This study was limited to males aged 20–23 years. To generalize this study, randomized controlled trials should be performed with participants of multiple age groups, including men and women. Full article

This article addresses the problem of formation control of a quadrotor and one (or more) marine vehicles operating at the surface of the water with the end goal of encircling the boundary of a chemical spill, enabling such vehicles to carry and release chemical dispersants used during ocean cleanup missions to break up oil molecules. Firstly, the mathematical models of the Medusa class of marine robots and quadrotor aircrafts are introduced, followed by the design of single vehicle motion controllers that allow these vehicles to follow a parameterised path individually using Lyapunov-based techniques. At a second stage, a distributed controller using event-triggered communications is introduced, enabling the vehicles to perform cooperative path following missions according to a pre-defined geometric formation. In the next step, a real-time path planning algorithm is developed that makes use of a camera sensor, installed on-board the quadrotor. This sensor enables the detection in the image of which pixels encode parts of a chemical spill boundary and use them to generate and update, in real time, a set of smooth B-spline-based paths for all the vehicles to follow cooperatively. The performance of the complete system is evaluated by resorting to 3-D simulation software, making it possible to visually simulate a chemical spill. Results from real water trials are also provided for parts of the system, where two Medusa vehicles are required to perform a static lawn-mowing path following mission cooperatively at the surface of the water. Full article

Among Renewable Energy Sources (RES), wind energy is emerging as one of the largest installed renewable-power-generating capacities. The technological maturity of wind turbines, together with the large marine wind resource, is currently boosting the development of offshore wind turbines, which can reduce the visual and noise impacts and produce more power due to higher wind speeds. Nevertheless, the increasing penetration of wind energy, as well as other renewable sources, is still a great concern due to their fluctuating and intermittent behavior. Therefore, in order to cover the mismatch between power generation and load demand, the stochastic nature of renewables has to be mitigated. Among proposed solutions, the integration of energy storage systems in wind power plants is one of the most effective. In this paper, a Hybrid Energy Storage System (HESS) is integrated into an offshore wind turbine generator with the aim of demonstrating the benefits in terms of fluctuation reduction of the active power and voltage waveform frequency, specifically at the Point of Common Coupling (PCC). A MATLAB^®/SimPowerSystems model composed of an offshore wind turbine interfaced with the grid through a full-scale back-to-back converter and a flywheel-battery-based HESS connected to the converter DC-link has been developed and compared with the case of storage absence. Simulations were carried out in reference to the wind turbine’s stress conditions and were selected—according to our previous work—in terms of the wind power step. Specifically, the main outcomes of this paper show that HESS integration allows for a reduction in the active power variation, when the wind power step is applied, to about 3% and 4.8%, respectively, for the simulated scenarios, in relation to more than 30% and 42% obtained for the no-storage case. Furthermore, HESS is able to reduce the transient time of the frequency of the three-phase voltage waveform at the PCC by more than 89% for both the investigated cases. Hence, this research demonstrates how HESS, coupled with renewable power plants, can strongly enhance grid safety and stability issues in order to meet the stringent requirements relating to the massive RES penetration expected in the coming years. Full article

Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are used to approximate the DNN controlling an autonomous surface vessel (ASV) in a simulated environment and then run in parallel with the DNN to give explanations in the form of feature attributions in real-time. How well a model can be understood depends not only on the explanation itself, but also on how well it is presented and adapted to the receiver of said explanation. Different end-users may need both different types of explanations, as well as different representations of these. The main contributions of this work are (1) significantly improving both the accuracy and the build time of a greedy approach for building LMTs by introducing ordering of features in the splitting of the tree, (2) giving an overview of the characteristics of the seafarer/operator and the developer as two different end-users of the agent and receiver of the explanations, and (3) suggesting a visualization of the docking agent, the environment, and the feature attributions given by the LMT for when the developer is the end-user of the system, and another visualization for when the seafarer or operator is the end-user, based on their different characteristics. Full article