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JMSE
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Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships
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Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships

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: 31 January 2025 | Viewed by 4673

Special Issue Editors

Prof. Dr. Chunhui Zhou

E-Mail Website
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

E-Mail Website
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

E-Mail Website
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 (6 papers)

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Research

21 pages, 7482 KiB  
Article
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
Viewed by 507
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 [...] Read more.
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  
Article
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
Viewed by 652
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 [...] Read more.
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  
Article
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 498
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 [...] Read more.
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  
Article
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
Viewed by 594
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 [...] Read more.
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  
Article
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
Viewed by 831
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 [...] Read more.
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  
Article
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
Viewed by 761
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 [...] Read more.
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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