Ship Navigation System Research Articles

Development of an Autonomous Ship Navigation System Based on the A*-AHP Hybrid Path Planning Method

Development of an Autonomous Ship Navigation System Based on the A*-AHP Hybrid Path Planning Method

A novel collaborative collision avoidance decision method for multi-ship encounters in complex waterways

With the continuous growth of maritime traffic volume, multi-ship collision avoidance has gradually become one of the most crucial issues in ensuring the safety of ship navigation. This study proposes a multi-ship collaborative collision avoidance decision model (MCCA) to address the issue of multi-ship encounters in complex waterways. The model takes into account the ship spatio-temporal collision risk, relative motion trends, as well as the safety and economic considerations of ship navigation. A comprehensive collision avoidance method is designed based on the velocity obstacle method, enabling simultaneous adjustments both course and speed, with the aim of minimizing economic losses incurred by ships due to avoidance operations while ensuring ship navigation safety. Meanwhile, the NSGA-II algorithm, which has faster convergence and better effect, is used to calculate the optimal collaborative collision avoidance decision of multiple ships. Besides, both simulation and example validation are used to test the validity of the model. The results indicate that the proposed model can effectively achieve multi-ship collaborative collision avoidance in the region. Simultaneously, it holds significant reference value for guaranteeing the safety of ship navigation, enhancing supervision efficiency, and promoting the development of intelligent ship navigation systems.

A robust method for multi object tracking in autonomous ship navigation systems

A novel Maritime Multi-Object Tracking method is proposed, combining a deep learning-based object detector with target association algorithms to achieve robust sea-surface multi-object tracking. Specifically, the Multi-Object Tracking employs You Only Look Once version 7 detector for object detection. In the data association part, a module for onboard camera motion compensation is developed, a maritime dynamic spatial information-based intersection-over-union is presented as a similarity metric, and a progressive refinement cascade matching strategy is designed to enhance the tracker's sea-surface multi-object tracking capabilities. The Jari Maritime Tracking Dataset is utilised to validate the effectiveness performance of the proposed method. Experimental results demonstrate that compared to the earlier process, the proposed method exhibits a significant enhancement in multiple object tracking accuracy, with an increase of 27.8% and an achieved score of 81.3. In particular, it reduces the number of identifications switching and missed targets, achieving holistically preferable performance. Meanwhile, the speed of the proposed Multi-Object Tracking fulfils the engineering application requirements for an autonomous ship navigation system.

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

Classification of Cyber Risks for Sea Vessel’s Voyage Cycle

Modern digital transformation in the shipping industry Shipping 4.0 is accompanied by increased automation and autonomy of the ship. This means strengthening the digitization of the ship's navigation systems (cyber systems). Therefore, the safe operation and navigation of modern ships depends on the adequate operation of cyber-physical systems, which are created on the basis of information and operational technologies. Such interconnections of ship information systems inevitably increase the vulnerability of the ship's digital navigation infrastructure to cyber-attacks. The article examines methods of controlling cyber risks of various origins, which determine the priority of flows, their internal interconnection and relations with the sources of messages that form them. The formalization of the qualitative and quantitative properties of information flows of the voyage cycle using matrix and graph-analytical modeling methods has been carried out. A decomposition of information was built and a cluster of information security of ship maneuvering control was defined, with its access closed for cyber-attacks. In the event of cyber-attacks, it is very important for the navigator to determine the moment of it’s appearance and the state of navigational devices of the bridge. Usually, the working state of the device is determined by the presence of electric current in it. However, only the presence of current in the device during cyber-attacks is not enough to assess its working condition. Inspection and verification of 11 navigation devices on the bridge takes a lot of time, which the navigator does not have in the event of a cyber-attack. Therefore, we propose to introduce an additional function of controlling the working status of each navigational device, and to transmit the results of all devices to a separate navigation cluster Mk of the navigation bridge via shielded cable lines of the vessel without other methods. This will make it possible to gather information about all the devices in one place on the bridge, protect it from cyberattacks, and quickly determine the operating status of all the navigation devices on the bridge. Descriptive modeling of the meaningful categories of the information space of maritime routes during cyberattacks was performed. The proposed classification of cyber risks has a logical structure based on the grouping of message flows, using the cluster connection coefficient between them. This makes it possible to assess the criticality of the impact of cyber-attacks on the ship and to choose ways to reduce their impact on the maneuvering of the ship in the voyage cycle.

Perancangan Aplikasi Desktop Monitoring Kapal dan Penyimpanan Data AIS dengan RTL-SDR dan Raspberry Pi Menggunakan Python dan PyQt

This research aims to develop a desktop application for Monitoring Ships and storing data from the Automatic Identification System (AIS) utilizing Realtek Software Defined Radio (RTL-SDR) and Raspberry Pi devices. The development method employed is the waterfall model, using Python and PyQt as the main programming languages. Black-box testing was conducted on the application, covering aspects such as decoding AIS messages, real-time monitoring, historical data storage, and statistical analysis of the data. The test results indicate that the application successfully addresses challenges faced by the simple AIS receiver, achieving the research objectives by accurately decoding AIS messages, monitoring ship journeys in real-time, storing AIS data as historical data, and presenting informative statistical data. Despite some lag, especially on the Raspberry Pi device, the application contributes positively with improved accuracy and functionality. Further evaluation, technological updates, and integration with other devices are expected to enhance the application's performance. The use of Python and PyQt provides a foundation for further development to enhance the quality of ship navigation systems. It is recommended to involve the decoding of other AIS message types in future research and to consider building a ship journey simulation system for an interactive user experience.

Implementation of DMAIC for Production Quality Control: Case Study of Power Supply Production in Indonesia

The level of competition in the manufacturing industry is getting tougher, making companies must be able to provide the best service to customers. One of the companies in Indonesia engaged in asset monitoring and ship navigation systems, has a production division that produces power supply products. Based on the analysis that has been carried out at the company that there are problems such as a damaged PCB connector that makes the product categorized as a manufacturing defect. This research aims to be able to find alternative improvements to production processes that have a high defect rate. The Define, Measure, Analyze, Improve, and Control (DMAIC) framework is used as a method to shape the mindset of management and employees to be able to solve these problems. Based on this framework, three causes of the problem were found, namely human factors, materials, and inappropriate methods during the production process. Therefore, appropriate corrective actions are taken, and a control process is carried out by comparing control charts, Defects Per Million Opportunities (DPMO) and sigma levels to determine the impact of changes from the improvements made. Quantitatively, there was an increase in the DPMO value of 39.47% and an increase in the sigma level of 8.64%. This shows that the DMAIC framework can provide the right solutions and preventing the same quality problems from happening again.

An Intelligent Scheduling System and Hybrid Optimization Algorithm for Ship Locks of the Three Gorges Hub on the Yangtze River

The ship lock system of the Three Gorges Hub(TGH) stands out as the most intricate navigation system for ships passing through dams, which is composed of the the world’s largest ship lock and ship lift. It operates with features like multi-dam configurations, multi-chambers, multi-operation modes and batch processing. This paper addresses the complex challenge of optimizing the lock schedule, ship entry timings, and ship placements within the lock chamber. By analyzing navigational rules, operational characteristics, and existing problems of TGH, this research introduces an intelligent scheduling system framework. With the incorporation of multiple objectives and constraints, a mixed-integer nonlinear programming(MINLP) model is formulated. Subsequently, a hybrid intelligent algorithm based on simulated annealing and heuristic search is constructed to achieve the optimization of the lock schedule. The algorithm efficacy is validated using the real-world data from TGH.

Development of trajectory-tracking maneuvering system for automatic berthing/unberthing based on double deep Q-network and experimental validation with an actual large ferry

The development of an automatic berthing and unberthing system is essential for realizing autonomous ship navigation systems. Although numerous berthing/unberthing methods using neural networks (NNs) have been proposed in recent years, whether NNs trained via numerical simulations including modeling errors can be used for the automatic berthing/unberthing of actual ships in natural external disturbances is not clear. Hence, in this study, a maneuvering system for automatic berthing/unberthing was developed using a NN that solves the trajectory-tracking problem. The proposed method considered future targets as discrete points in learning, such as to absorb the modeling error and reduce the influence of external disturbances; thus, it achieved a more robust control compared to path planning methods. The system achieved trajectory tracking at low speeds by offline learning without teaching data to follow a target ship with the same maneuverability as its own ship and operated randomly under external disturbances. Furthermore, an online target correction logic function that locally corrects targets was developed to increase the tracking ability in external disturbances. Subsequently, a berthing and unberthing experiment was conducted using an actual large ferry to ensure the applicability of simulation-based NNs to the trajectory-tracking control of an actual ship in a real-life environment.

Fast and Intelligent Ice Channel Recognition Based on Row Selection

The recognition of ice channels plays a crucial role in developing intelligent ship navigation systems in ice-covered waters. Navigating through ice channels with the assistance of icebreakers is a common operation for merchant ships. Maneuvering within such narrow channels presents a significant challenge for the captain’s skills and ship performance. Therefore, it becomes essential to explore methods for enabling ships to navigate through these channels automatically. A key step in achieving this is the accurate recognition and extraction of boundary lines on both sides of the ice channel. An ice channel line recognition method based on the lane line detection algorithm UFAST is implemented. The method is trained and tested on the constructed ice channel dataset, with the test results showing that the average recognition accuracy reaches 84.1% and the recognition speed reaches 138.3 frames per second, meeting the real-time requirement. In order to solve the current lack of authentic ice channel images, ice channel navigation scenes are built based on UE4, and synthetic ice channel images are rendered. The method in this paper is also compared with the traditional non-intelligent Otsu threshold segmentation method and the intelligent instance segmentation method YOLACT for performance analysis. The method in this paper has 9.5% higher ice channel recognition accuracy and 103.7 frames per second higher recognition speed compared with YOLACT. Furthermore, ablation studies are conducted to analyze the relationship between the number of gridding cells in the proposed method and ice channel recognition accuracy.

TPS-Genetic Algorithm for Real-Time Sailing Route Planning based on Potential Field Theory

Real-time route planning is always a difficulty in maritime traffic. Route planning must take into account the complex meteorological environment. Route planning based on meteorological environment real-time update is the basis of route feasibility. Based on potential field theory, this paper proposes a TPS - Genetic algorithm for real-time route planning of sailing ships. On the basis of genetic algorithm, combined with the characteristics of route planning, the turning point sorting operation is added to improve the calculation efficiency, and further improve the real-time performance of route planning. Simulation experiments are established and compared with A* algorithm. The experimental results show that the potential field theory can accurately express the dynamic changes of Marine meteorology, and the path planned by TPS - Genetic algorithm is more suitable for real-time navigation environment. TPS - Genetic algorithm can be applied to ship navigation system, which can further adjust the potential energy base and plan routes according to the needs of shipping companies.

A Receding Horizon Navigation and Control System for Autonomous Merchant Ships: Reducing Fuel Costs and Carbon Emissions under the Premise of Safety

In order to solve the multi-objective planning and trajectory tracking control problem related to maritime autonomous surface ships (MASSs), a new design scheme for autonomous navigation is proposed in this paper, with a receding horizon navigation and control (RHNC) system that contains navigation and control modules. In the navigation module, we designed a superposition field gradient descent local search algorithm based on the cost field, emission field, and guidance field to navigate the MASS reference path, and in the control module, we designed a nonlinear controller that can handle multiple constraints based on the NMPC framework. Under the new scheme, the navigation module completes local path planning to reduce costs and emissions, the control module accomplishes accurate trajectory tracking and real-time collision avoidance, and the information is transmitted in both directions between the two modules to collaboratively complete the MASS navigation and control tasks. We conducted a simulation study of the navigation algorithm and controller and the autonomous navigation system using a Kriso Container ship (KCS). The simulation results demonstrate the effectiveness of the proposed cooperative design scheme in reducing navigation costs and emissions and avoiding autonomous collision avoidances.

INFORMATION SECURITY ARRANGEMENTS OF SEAGOING SHIP

Security of modern transport ships is primarily related to the protection of informationfrom unauthorized access and prevention of leakage. Every shipowner, by protecting theconfidentiality of data, not only reduces the risk and possible losses from its leakage, but alsoincreases the level of trust in the eyes of its customers and partners. The quality and quantity changes,which have taken place in recent years on seagoing vessels, have significantly contributed to thesafety of their operation and the smooth running of their operations. Modern ship navigation isheavily dependent on information support, which is an important element in ship handling processes.New marine digital information systems are being deployed based on digital data exchange, to ensurenavigational safety. This allows immediate processing of summaries of information for thefunctioning of the ship's navigation systems. In addition, these systems provide the ability to exclude the decision-making process for ship management based on incomplete or unreliable information andrequire the maintenance of special measures to ensure its safety. Therefore, the task in the sphere ofcyber security in maritime transport is the development of means of protection of informationresources of the vessel, which is one of the first and urgent. Given these factors and the currentsituation, the international maritime organization has developed and adopted a number of maritimeindustry security documents that are designed to address a number of provisions and issues relatedto cybersecurity of ship and ship operations. The purpose of this article is to analyze main factorsinfluencing ship's information security. And on the basis of the presented conceptual model of shipsecurity the main factors influencing the information security of the ship are identified.

Study on the Risk Model of the Intelligent Ship Navigation

The perception of risks is a prerequisite for establishing an intelligent ship navigation system based on ship-shore cooperation. According to the research conclusions on the ship-shore collaboration, the technical characteristics of the intelligent ship navigation system, and the analysis of the test results of the ship “ZHI FEI” featured with intelligent navigation, we study and construct the conceptual model and mathematical expression model for risk evolution of the intelligent ship navigation. We further explore the unmanned trend, the general law, and the characteristics of risk evolution based on navigation scenarios and chain effects. The present study provides insight into the risk perception, management, and government governance of intelligent ship navigation and the construction of an intelligent ship navigation system based on ship-shore collaboration.

Improved parametrized multiple window spectrogram with application in ship navigation systems

In analyzing non-stationary noisy signals with time-varying frequency content, it's convenient to use distribution methods in joint, time and frequency, domains. Besides different adaptive data-driven time-frequency (TF) representations, the approach with multiple orthogonal and optimally concentrated Hermite window functions is an effective solution to achieve a good trade-off between low variance and minimized stable bias estimates. In this paper, we propose a novel spectrogram method with multiple optimally parameterized Hermite window functions, with parameterization which includes a pair of free parameters to regulate the shape of the window functions. The computation is performed in the optimization process to minimize the variable projection (VP) functional problem. The proposed parametrized distribution method improves TF concentration and instantaneous frequency (IF) estimation accuracy, as shown in experimental results for synthetic signals and real-life ship motion response signals. With the optimization of nonlinear least-squares approximation of the ship response signals, the Hermite spectra are centralized, and only up to 15 basis functions are sufficient for concentration improvement in the TF domain.

Ship images detection and classification based on convolutional neural network with multiple feature regions

In recent years, the maritime industry is developing rapidly, which poses great challenges for intelligent ship navigation systems to achieve accurate ship classification. To cope with this problem, a Recurrent Attention Convolutional Neural Network (RA-CNN) is proposed, which is fused with multiple feature regions for ship classification. The proposed model has three scale layers, each of which contains a classification network VGG-19 and a localisation head Attention Proposal Network (APN). First, the Scale Dependent Pooling algorithm is integrated with VGG-19 to reduce the impact of over-pooling and improve the classification performance of small ships. Second, the APN incorporates the Joint Clustering algorithm to generate multiple independent feature regions; thus, the whole model can make full use of the global information in ship recognition. In the meantime, the Feature Regions Optimisation method is designed to solve the overfitting problem and reduce the overlap rate of multiple feature regions. Finally, a novel loss function is defined to cross-train VGG-19 and APN, which accelerates the convergence process. The experimental results show that the classification accuracy of the authors’ proposed method reaches 90.2%, which has a 6% improvement over the baseline RA-CNN. Both classification accuracy and robustness are improved by a large margin compared to those of other compared models.

Survey on Deep Learning-Based Marine Object Detection

We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

Perceptual Fusion of Electronic Chart and Marine Radar Image

Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.

선박 사이버보안 강화를 위한 입법론적 연구

In modern ships, advanced information and communication technology is applied to the ship navigation system, which brings economic efficiency, safety, and eco-friendliness to ship navigation, while increasing the risk to ship cybersecurity, ultimately providing the cause of cybersecurity accidents. Such ship cybersecurity incidents can disrupt the operating systems of ships and ports, which incurs enormous time and cost damage. Therefore, the purpose of this study is to prepare a basis for countermeasures through reorganization of related laws in order to strengthen ship cybersecurity. Recently, the International Maritime Organization and classifications of each country have developed and distributed guidelines on ship cyber security to strengthen ship cyber security, and enforced them to be enforced by law. However, there is currently no legal system related to ship cyber security in Korea, and as a result, systematic responses to ship cyber security incidents are insufficient. Therefore, in order to prepare the legal basis for ship cyber security, it is absolutely necessary to reorganize the amendment of the ship cyber security-related legislation. In this study, trends of the International Maritime Organization, classification, shipping industry, and major shipping countries were investigated to examine international responses to ship cybersecurity. Therefore, the results of this study can be utilized for the amendment of the ship cyber security-related laws in the future.

Research on Computer Image Processing Technology in Navigation and Navigation

In view of the existing navigation systems used on ships, the Baidoo satellite signal access recognition rate is low, and the target recognition accuracy is poor. The paper proposes a computer image recognition method for the positioning target of the ship navigation system based on the Baidoo satellite. In view of the large resource consumption and low calculation rate of the traditional method in the calculation process of the Baidoo satellite signal, the compressed sensing image processing algorithm of single-layer wavelet transform is introduced to perform directional exclusive calculation on the Baidoo satellite signal. The research in the thesis found that the Baidoo satellite ship navigation system positioning target accurate recognition method based on the single-layer wavelet transform-based compressed sensing image processing algorithm can quickly, effectively and accurately lock and identify the set target in the Baidoo satellite navigation.