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Artificial Intelligence and Its Applications in Intelligent Ship Navigation
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Artificial Intelligence and Its Applications in Intelligent Ship Navigation

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: 1 May 2025 | Viewed by 4479

Special Issue Editors

Dr. Chenguang Liu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: ship intelligent navigation; motion planning; motion control; formation control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jialun Liu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: testing of intelligent ships; intelligent navigation; ship motion modelling and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiumin Chu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: intelligent ship; navigation control; environment prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Lang

E-Mail Website
Guest Editor
Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
Interests: maritime digitalization; sustainable shipping; energy efficiency measures; machine learning

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) continues to develop, autonomous ships have attracted increased amounts of attention with the intention of downsizing the number of staff, increasing efficiency, etc. The deep learning or reinforcement learning network allows more possibilities to improve the intelligence level of ship navigation, which could realize human-like performance in the process of environment perception, decision-making, collision avoidance, and motion control (including berthing and unberthing). Therefore, AI in intelligent ship navigation can boost more applications to assist and even replace officers on board, which is the trend in future autonomous ships in inland waterways and oceans.

In this Special Issue, we welcome contributions from a broad range of theoretical, modeling, field, and laboratory research focused on processes that affect intelligent ships, including, but not limited to, the following topics:

  • Ship intelligent perception with radar, camera, AIS, etc., alone or in combination for the ship navigation environment;
  • Deep learning network for decision-making of ship navigation;
  • Ship navigation behavior analysis;
  • Situation awareness analysis and prediction;
  • Human–machine cooperative navigation;
  • Intelligent collision avoidance with complex encounter scenarios;
  • Ship motion planning with reinforcement learning or other AI algorithms;
  • Ship motion prediction and control, including sailing and berthing;
  • Shore-based remote control;
  • Testing of intelligent ship navigation.

Dr. Chenguang Liu
Prof. Dr. Jialun Liu
Prof. Dr. Xiumin Chu
Dr. Xiao Lang
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 intelligent perception
  • deep learning network for navigation
  • ship navigation behavior analysis
  • navigation situation awareness
  • human–machine cooperative navigation
  • autonomous collision avoidance
  • reinforcement learning for motion planning
  • ship sailing and berthing control
  • shore-based remote control
  • testing of intelligent navigation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

21 pages, 2250 KiB  
Article
Optimization of Controllable-Pitch Propeller Operations for Yangtze River Sailing Ships
by Wuliu Tian, Xiao Lang, Chi Zhang, Songyin Yan, Bing Li and Shuo Zang
J. Mar. Sci. Eng. 2024, 12(9), 1579; https://doi.org/10.3390/jmse12091579 - 6 Sep 2024
Viewed by 797
Abstract
The Yangtze River’s substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing [...] Read more.
The Yangtze River’s substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing energy efficiency. We developed an optimization framework to determine the ideal pitch angle and rotation speed (RPM) under different sailing conditions. The energy performance model for inland ships was enhanced to account for the open-water efficiency of CPPs across various pitch angles and RPMs, considering the impacts of current and shallow water, among other factors. The optimization approach was refined by incorporating an improved genetic algorithm with an annealing algorithm to enhance the initial population, applying the K-means clustering algorithm for population segmentation, and using multi-parent crossover from diverse clusters. The efficacy of the optimization method for CPP operations was validated by analyzing three operational scenarios of a Yangtze sailing ship. Additionally, key components of the ship performance model were calibrated through experimental tests, demonstrating an anticipated fuel consumption reduction of approximately 5% compared to conventional fixed-pitch propellers. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)
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21 pages, 4836 KiB  
Article
A Novel Dynamically Adjusted Entropy Algorithm for Collision Avoidance in Autonomous Ships Based on Deep Reinforcement Learning
by Guoquan Chen, Zike Huang, Weijun Wang and Shenhua Yang
J. Mar. Sci. Eng. 2024, 12(9), 1562; https://doi.org/10.3390/jmse12091562 - 5 Sep 2024
Viewed by 710
Abstract
Decision-making for collision avoidance in complex maritime environments is a critical technology in the field of autonomous ship navigation. However, existing collision avoidance decision algorithms still suffer from unstable strategy exploration and poor compliance with regulations. To address these issues, this paper proposes [...] Read more.
Decision-making for collision avoidance in complex maritime environments is a critical technology in the field of autonomous ship navigation. However, existing collision avoidance decision algorithms still suffer from unstable strategy exploration and poor compliance with regulations. To address these issues, this paper proposes a novel autonomous ship collision avoidance algorithm, the dynamically adjusted entropy proximal policy optimization (DAE-PPO). Firstly, a reward system suitable for complex maritime encounter scenarios is established, integrating the International Regulations for Preventing Collisions at Sea (COLREGs) with collision risk assessment. Secondly, the exploration mechanism is optimized using a quadratically decreasing entropy method to effectively avoid local optima and enhance strategic performance. Finally, a simulation testing environment based on Unreal Engine 5 (UE5) was developed to conduct experiments and validate the proposed algorithm. Experimental results demonstrate that the DAE-PPO algorithm exhibits significant improvements in efficiency, success rate, and stability in collision avoidance tests. Specifically, it shows a 45% improvement in success rate per hundred collision avoidance attempts compared to the classic PPO algorithm and a reduction of 0.35 in the maximum collision risk (CR) value during individual collision avoidance tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)
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16 pages, 3698 KiB  
Article
Analysis of Bi-LSTM CRF Series Models for Semantic Classification of NAVTEX Navigational Safety Messages
by Changui Lee, Hoyeon Cho and Seojeong Lee
J. Mar. Sci. Eng. 2024, 12(9), 1518; https://doi.org/10.3390/jmse12091518 - 2 Sep 2024
Viewed by 595
Abstract
NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems [...] Read more.
NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)
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19 pages, 963 KiB  
Article
Optimization of Sailing Speed for Inland Electric Ships Based on an Improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm
by Kang Zhang , Chenguang Liu , Zhibo He , Huimin Chen , Qian Xiang  and Xiumin Chu 
J. Mar. Sci. Eng. 2024, 12(8), 1417; https://doi.org/10.3390/jmse12081417 - 17 Aug 2024
Cited by 1 | Viewed by 762
Abstract
Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper [...] Read more.
Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)
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24 pages, 27895 KiB  
Article
Informer-Based Model for Long-Term Ship Trajectory Prediction
by Caiquan Xiong, Hao Shi, Jiaming Li, Xinyun Wu and Rong Gao
J. Mar. Sci. Eng. 2024, 12(8), 1269; https://doi.org/10.3390/jmse12081269 - 28 Jul 2024
Cited by 1 | Viewed by 990
Abstract
Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) [...] Read more.
Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) due to difficulties in capturing long-term dependencies, resulting in significant prediction errors. This paper proposes the Informer-TP method, leveraging Automatic Identification System (AIS) data and based on the Informer model, to enhance the ability to capture long-term dependencies, thereby improving the accuracy of long-term ship trajectory predictions. Firstly, AIS data are preprocessed and divided into trajectory segments. Secondly, the time series is separated from the trajectory data in each segment and input into the model. The Informer model is utilized to improve long-term ship trajectory prediction ability, and the output mechanism is adjusted to enable predictions for each segment. Finally, the proposed model’s effectiveness is validated through comparisons with baseline models, and the influence of various sequence lengths Ltoken on the Informer-TP model is explored. Experimental results show that compared with other models, the proposed model exhibits the lowest Mean Squared Error, Mean Absolute Error, and Haversine distance in long-term forecasting, demonstrating that the model can effectively capture long-term dependencies in the trajectories, thereby improving the accuracy of long-term vessel trajectory predictions. This provides an effective and feasible method for ensuring ship navigation safety and advancing intelligent shipping. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)
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