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A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 12565

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Special Issue Editors

Prof. Dr. Chunhui Zhou

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Guest Editor

School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: green shipping; low-carbon transportation; safe navigation; big data
Special Issues, Collections and Topics in MDPI journals

Dr. Yixiong He

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Guest Editor

School of Navigation, Wuhan University of Technology, Wuhan 430062, China
Interests: autonomous collision avoidance of ships; intelligent navigation; autonomous navigation of ships; modeling of ship maneuvering motion; ship motion control

Dr. Liang Huang

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Guest Editor

Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China
Interests: maritime big data; intelligent maritime supervision; behavior analysis and prediction

Special Issue Information

Dear Colleagues,

The technological trends of intelligence, greening, and efficiency are deeply driving high-quality development in the shipping industry. As a crucial tool in maritime transportation, the safe maneuvering, efficient navigation, and intelligent management of ships have become inevitable trends in current development. Over the past few decades, methods and technologies such as machine learning, image recognition, big data mining, and computer simulation have propelled the research and application of autonomous ships, unmanned surface vehicles (USVs), and ship monitoring systems, providing feasibility for the intelligent development of ships. In the near future, with the strong momentum of smart shipping and green shipping, ships will become safer, more efficient, and smarter. This Special Issue focuses on the challenges and innovations related to safe maneuvering, efficient navigation, and intelligent management of ships, revealing the latest issues and research progress from the perspectives of policy, technology, and methods. The aim is to advance our understanding of the development of vessel safety, efficiency, and intelligence using frontier systems theory and methods, thereby driving the development of the shipping industry.

High-quality papers presenting research in this area of study will be considered, with a specific focus in issues such as, but not limited to, the following:

Collision avoidance for autonomous surface vessels;
Risk assessment for ship operation;
Ship autonomous berthing;
Decision making for the autonomous navigation;
Ship movement predictions;
Navigational safety in complex waters;
Intelligent scheduling for ships;
Maritime traffic modelling and simulation methods;
Deep learning applications in maritime traffic situational awareness;
Ship behaviour evaluation and prediction;
New technologies for green ships.

Prof. Dr. Chunhui Zhou
Dr. Yixiong He
Dr. Liang Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

ship safety
ship navigation
intelligent shipping
ship maneuvering

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Published Papers (12 papers)

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Research

14 pages, 5361 KiB

Open AccessArticle

Research on the Impact of Deep Sea Offshore Wind Farms on Maritime Safety

by Wenbo Yu, Jian Liu, Pengcheng Yan and Xiaobin Jiang

J. Mar. Sci. Eng. 2025, 13(4), 699; https://doi.org/10.3390/jmse13040699 - 31 Mar 2025

Viewed by 290

Abstract

With the rapid development of offshore wind farms, the construction of deep sea wind farms has increasingly significant impacts on the safety of maritime navigation. This paper conducts a cluster analysis of ship trajectories based on AIS data to analyze the characteristics of ship traffic flow in the waters near the Shanghai deep sea offshore wind farm. A fuzzy hierarchical analysis method is proposed. Combined with the layout of wind farms and the navigational environment, a risk assessment model for offshore wind farm navigation is established. This model quantifies the factors that affect the safety of ship navigation due to the wind farm and evaluates the navigation risks in the surrounding waters. The results of the research show that the construction of wind farms increases traffic density, interferes with traditional shipping routes, and consequently increases the risk of collisions. The fuzzy hierarchical analysis method has good operability and feasibility in the safety assessment of offshore wind farms, and can provide effective support for future safety assessment of offshore wind farms. The sections are arranged as follows: Firstly, the background and significance of the paper are introduced, as well as the current research status. Secondly, an overview of Shanghai offshore wind farms and their nearby shipping routes is introduced, and then the risk situation of existing wind farms is pointed out. Then the risk assessment method is carried out, and the navigational risk of offshore wind farms is evaluated. Finally, the paper proposes measures to reduce the navigational risk of ships in the vicinity of wind farms. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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24 pages, 3767 KiB

Open AccessArticle

Research on the Coupling Dynamics Characteristics of Underwater Multi-Body Separation Considering the Influence of Elastic Constraints

by Jiahui Chen, Yanhua Han, Ruofan Li, Zhenmin He and Yong Zhang

J. Mar. Sci. Eng. 2025, 13(4), 627; https://doi.org/10.3390/jmse13040627 - 21 Mar 2025

Viewed by 190

Abstract

Based on the Newton–Euler method, a multi-body coupling dynamics model of the separation process of underwater vehicles is established. The conditions of contact and detachment between the sub-vehicle and each group of elastic gaskets are analyzed in detail, and the elastic gasket constraint model is established to simulate the elastic contact and detachment process. Based on the Computational Fluid Dynamics (CFD) method, the hydrodynamic data of vehicles under different cases is calculated. In this context, a relatively accurate hydrodynamic database is established, where the hydrodynamic of the Unmanned Underwater Vehicle (UUV) is obtained through fitting, while those of the sub-vehicles are calculated using online interpolation. These provide conditions for realizing Fluid–Structure Interaction (FSI) calculation. Utilizing the FSI simulation method in the multi-body separation process, the separation dynamics of the multi-vehicle under the influence of elastic constraint parameters are analyzed. The simulation results show that the pitching attitude angles of the UUV and sub-vehicle in the separation process are negatively correlated with the change of elastic constraint stiffness, and the load is positively correlated with it, which are in opposite optimization directions. When the total stiffness of the elastic gaskets remains constant, changes in the number of elastic gaskets have a minimal impact on the UUV and sub-vehicle motion state during separation, but significantly affects the load fluctuations on the sub-vehicle, leading to structural vibration issues. The analysis method established in this paper is capable of quickly assessing the safety of underwater vehicle separation for different elastic gasket schemes, thereby facilitating the optimization of parameters. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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27 pages, 3487 KiB

Open AccessArticle

Cooperative Formation Control of Multiple Ships with Time Delay Conditions

by Wei Tao, Jian Tan, Zhongyi Sui, Lizheng Wang and Xin Xiong

J. Mar. Sci. Eng. 2025, 13(3), 549; https://doi.org/10.3390/jmse13030549 - 12 Mar 2025

Viewed by 384

Abstract

The cooperative control of multiple autonomous surface vehicles (ASVs) is a critical area of research due to its significant applications in maritime operations, such as search and rescue and environmental monitoring. However, challenges such as communication delays and dynamic topologies often hinder stable cooperative control in practical scenarios. This study addresses these challenges by developing a formation control method based on consensus theory, focusing on both formation control and time delay. First, a simplified ASV characteristic model is established, and a basic consensus control algorithm is designed and analyzed for stability, considering different communication topologies. Then, to handle delays, the formation control method is extended, and the stability of the revised algorithm is rigorously proven using the Lyapunov function. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays. In the end, comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays, with a convergence time of approximately 100 s and a formation error stabilizing at around 7 m. This research lays a foundation for more reliable cooperative control systems for ships, with potential applications in a variety of maritime and autonomous systems. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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35 pages, 37221 KiB

Open AccessArticle

Target Ship Recognition and Tracking with Data Fusion Based on Bi-YOLO and OC-SORT Algorithms for Enhancing Ship Navigation Assistance

by Shuai Chen, Miao Gao, Peiru Shi, Xi Zeng and Anmin Zhang

J. Mar. Sci. Eng. 2025, 13(2), 366; https://doi.org/10.3390/jmse13020366 - 16 Feb 2025

Viewed by 1043

Abstract

With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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17 pages, 3093 KiB

Open AccessArticle

Reliability of Inland Water Transportation Complex Network Based on Percolation Theory: An Empirical Analysis in the Yangtze River

by Dong Han, Zhongyi Sui, Changshi Xiao and Yuanqiao Wen

J. Mar. Sci. Eng. 2024, 12(12), 2361; https://doi.org/10.3390/jmse12122361 - 22 Dec 2024

Viewed by 1093

Abstract

Inland water transportation is regarded as a crucial component of global trade, yet its reliability has been increasingly challenged by uncertainties such as extreme weather, port congestion, and geopolitical tensions. Although substantial research has focused on the structural characteristics of inland water transportation networks, the dynamic responses of these networks to disruptions remain insufficiently explored. This gap in understanding is critical for enhancing the resilience of global transportation systems as trade volumes grow and risks intensify. In this study, percolation theory was applied to evaluate the reliability of the Yangtze River transportation network. Ship voyage data from 2019 were used to construct a complex network model, and simulations of node removal were performed to identify key vulnerabilities within the network. The results showed that the failure of specific nodes significantly impacts the network’s connectivity, suggesting which nodes should be prioritized for protection. This research offers a dynamic framework for the assessment of inland water transportation network reliability and provides new insights that could guide policy decisions to improve the resilience of critical waterway systems. By identifying potential points of failure, this study contributes to the development of a more robust global trade infrastructure. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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17 pages, 3501 KiB

Open AccessArticle

The Impact of Offshore Wind Farm Construction on Maritime Traffic Complexity: An Empirical Analysis of the Yangtze River Estuary

by Jian Liu, Wenbo Yu, Zhongyi Sui and Chunhui Zhou

J. Mar. Sci. Eng. 2024, 12(12), 2232; https://doi.org/10.3390/jmse12122232 - 5 Dec 2024

Viewed by 1148

Abstract

The rapid growth of offshore wind farms (OWFs) as renewable energy sources has heightened concerns about maritime traffic safety and management in high-density traffic zones. These areas, characterized by complex interactions among diverse ship types and spatial constraints, require advanced situational awareness to prevent collisions and ensure efficient operations. Traditional maritime traffic systems often lack the granularity to assess the multifaceted risks around OWFs. Existing research has explored local traffic patterns and collision risks but lacks comprehensive frameworks for evaluating traffic complexity at both micro and macro levels. This study proposes a new complexity assessment model tailored to OWF areas, integrating micro-level ship interactions and macro-level traffic flow conditions to capture a holistic view of traffic dynamics. Using extensive historical AIS data from the Yangtze River Estuary, the model evaluates the impact of the proposed OWF on existing traffic complexity. The results demonstrate that OWFs increase navigational complexity, particularly in route congestion, course adjustments, and encounter rates between ships. Different ship types and sizes were also found to experience varying levels of impact, with larger ships and tankers facing greater challenges. By providing a quantitative framework for assessing traffic complexity, this research advances the field’s ability to understand and manage the risks associated with OWFs. The findings offer actionable insights for maritime authorities and OWF operators, supporting more effective traffic management strategies that prioritize safety and operational efficiency in high-density maritime areas. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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20 pages, 7482 KiB

Open AccessArticle

PID Controller Based on Improved DDPG for Trajectory Tracking Control of USV

by Xing Wang, Hong Yi, Jia Xu, Chuanyi Xu and Lifei Song

J. Mar. Sci. Eng. 2024, 12(10), 1771; https://doi.org/10.3390/jmse12101771 - 6 Oct 2024

Cited by 4 | Viewed by 1079

Abstract

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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17 pages, 4687 KiB

Open AccessArticle

Research on LSTM-Based Maneuvering Motion Prediction for USVs

by Rong Guo, Yunsheng Mao, Zuquan Xiang, Le Hao, Dingkun Wu and Lifei Song

J. Mar. Sci. Eng. 2024, 12(9), 1661; https://doi.org/10.3390/jmse12091661 - 16 Sep 2024

Cited by 1 | Viewed by 1126

Abstract

Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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29 pages, 12839 KiB

Open AccessArticle

Dynamic Calculation Approach of the Collision Risk in Complex Navigable Water

by Yihan Chen, Qing Yu, Weiqiang Wang and Xiaolie Wu

J. Mar. Sci. Eng. 2024, 12(9), 1605; https://doi.org/10.3390/jmse12091605 - 10 Sep 2024

Viewed by 1058

Abstract

It is vital to analyze ship collision risk for preventing collisions and improving safety at sea. This paper takes Ningbo-Zhoushan Port, a typical complex navigable water, as the research object. Firstly, a probabilistic conflict detection method based on an AIS data-driven dynamic ship domain model is proposed to achieve effective ship conflict detection under uncertain environments. Then, a ship group identification method is proposed, which can extract the ship groups with conflict correlation and space compactness. Finally, according to the characteristics of ship traffic in complex navigable waters, the dynamic calculation of ship collision risk is carried out from individual, regional, and local multi-scale perspectives. The experimental results show that the proposed method can detect the collision risk in a timely, reliable, and effective manner under complex dynamic conditions. As such, they provide valuable insights into ship collision risk prediction and the development of risk mitigation measures. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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22 pages, 3686 KiB

Open AccessArticle

Simulation Modeling for Ships Entering and Leaving Port in Qiongzhou Strait Waters: A Multi-Agent Information Interaction Method

by Dong Han, Xiaodong Cheng, Hualong Chen, Changshi Xiao, Yuanqiao Wen and Zhongyi Sui

J. Mar. Sci. Eng. 2024, 12(9), 1560; https://doi.org/10.3390/jmse12091560 - 5 Sep 2024

Cited by 1 | Viewed by 1270

Abstract

Simulation technology has been extensively utilized in the study of ship entry and exit from ports, as well as navigation through waterways. It effectively mirrors the stochastic dynamic changes and interrelationships among various elements within the port system. This paper provides a comparative analysis of the advantages and disadvantages of various modeling methods used in ship navigation simulations. It proposes a simulation modeling approach for ship–port systems based on multi-agent information interaction, which simulates the entire process of ships entering and exiting ports and navigating through complex waterways, achieving a precise and detailed simulation of the entire port entry and exit process in complex waters. Using the Qiongzhou Strait as a case study, the validity and accuracy of the model are verified. The model is employed to quantitatively identify port navigation elements, assess waterway capacity, and evaluate port operational capability. Furthermore, the model enables the analysis of coordination among port channels, berths, and anchorages. Based on simulation results and port development plans, recommendations are provided to enhance port service levels and promote scientific, rational development and efficient operation of ports. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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16 pages, 4301 KiB

Open AccessArticle

Analysis of Carbon Emission Reduction Paths for Ships in the Yangtze River: The Perspective of Alternative Fuels

by Chunhui Zhou, Wuao Tang, Yiran Ding, Hongxun Huang and Honglei Xu

J. Mar. Sci. Eng. 2024, 12(6), 947; https://doi.org/10.3390/jmse12060947 - 5 Jun 2024

Cited by 4 | Viewed by 1283

Abstract

In recent years, carbon emission reduction in the shipping sector has increasingly garnered scholarly attention. This study delves into the pathways for carbon emission reduction in shipping across the Yangtze River, emphasizing fuel alternatives. It initiates by introducing a novel ship carbon emission calculation methodology predicated on voyage data, followed by the development of a predictive model for ship carbon emissions tailored to specific voyages. Then, emission reduction scenarios for various voyage categories are designed and exemplary alternative fuels selected to assess their potential for emission mitigation. Subsequently, scenario analysis is employed to scrutinize the CO₂ emission trajectories under diverse conditions, pinpointing the most efficacious route for carbon emission abatement for inland vessels. Finally, the proposed method is applied to the middle and lower reaches of the Yangtze River. The results indicate that accelerating the adoption of alternative fuels for long-distance cargo ships would greatly accelerate the development of environmentally friendly shipping. Under a scenario prioritizing zero-carbon growth, emissions from inland vessels are anticipated to reach their zenith by 2040. These findings can provide theoretical guidance for emission reductions in inland shipping and effectively promote the green and sustainable development of the shipping sector. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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28 pages, 5727 KiB

Open AccessArticle

Ontology-Based Method for Identifying Abnormal Ship Behavior: A Navigation Rule Perspective

by Chunhui Zhou, Kunlong Wen, Junnan Zhao, Ziyuan Bian, Taotao Lu, Myo Ko Ko Latt and Chengli Wang

J. Mar. Sci. Eng. 2024, 12(6), 881; https://doi.org/10.3390/jmse12060881 - 26 May 2024

Cited by 4 | Viewed by 1222

Abstract

Navigation rules are critical for regulating ship behavior, and effective water traffic management requires accurate identification of ships exhibiting abnormal behavior that violates these rules. To address this need, this paper presents an ontology-based method for identifying abnormal ship behavior. First, we analyzed navigation rules (local regulations) to extract key elements. Next, based on this extraction, we built a navigation rule ontology that categorized ship behavior into state behavior (ship behavior at a specific time point) and process behavior (ship behavior in a time interval). We then constructed an abnormal ship behavior ontology, defined using topological relationships and navigation rules. Finally, we constructed inference rules to detect abnormal ship behaviors by using SWRL (Semantic Web Rule Language) and validated the effectiveness of the method with ship instances. The experimental results demonstrate that this method can accurately infer ships’ behaviors that deviate from established navigation rules. This research has significant implications for reducing waterborne traffic accidents, improving navigational safety, and safeguarding maritime traffic. Full article

(This article belongs to the Special Issue Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships)

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