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Artificial Intelligence and Its Applications in Intelligent Ship Navigation

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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: 1 May 2025 | Viewed by 8099

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Special Issue Editors

Dr. Chenguang Liu

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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

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State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: autonomous ship navigation and testing
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Prof. Dr. Xiumin Chu

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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

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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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Published Papers (8 papers)

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Research

25 pages, 12414 KiB

Open AccessArticle

Investigation into the Prediction of Ship Heave Motion in Complex Sea Conditions Utilizing Hybrid Neural Networks

by Yuchen Liu, Xide Cheng, Kunyu Han, Zhechun Liu and Baiwei Feng

J. Mar. Sci. Eng. 2025, 13(1), 1; https://doi.org/10.3390/jmse13010001 - 24 Dec 2024

Viewed by 458

Abstract

While navigating at sea, ships are influenced by various factors, including wind, waves, and currents, which can result in heave motion that significantly impacts operations and potentially leads to accidents. Accurate forecasting of ship heaving is essential to guarantee the safety of maritime navigation. Consequently, this paper proposes a hybrid neural network method that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory Networks (BiLSTMs), and an Attention Mechanism to predict the heaving motion of ships in moderate to complex sea conditions. The data feature extraction ability of CNNs, the temporal analysis capabilities of BiLSTMs, and the dynamic adjustment function of Attention on feature weights were comprehensively utilized to predict a ship’s heave motion. Simulations of a standard container ship’s motion time series under complex sea state conditions were carried out. The model training and validation results indicate that, under sea conditions 4, 5, and 6, the CNN-BiLSTM-Attention method demonstrated significant improvements in MAPE, APE, and RMSE when compared to the traditional LSTM, Attention, and LSTM-Attention methods. The CNN-BiLSTM-Attention method could enhance the accuracy of the prediction. Heave displacement, pitch displacement, pitch velocity, pitch acceleration, and incoming wave height were chosen as key input features. Sensitivity analysis was conducted to optimize the prediction performance of the CNN-BiLSTM-Attention hybrid neural network method, resulting in a significant improvement in MAPE and enhancing the accuracy of ship motion prediction. The research presented in this paper establishes a foundation for future studies on ship motion prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)

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25 pages, 8429 KiB

Open AccessArticle

Vessel Type Recognition Using a Multi-Graph Fusion Method Integrating Vessel Trajectory Sequence and Dependency Relations

by Lin Ye, Xiaohui Chen, Haiyan Liu, Ran Zhang, Bing Zhang, Yunpeng Zhao and Dewei Zhou

J. Mar. Sci. Eng. 2024, 12(12), 2315; https://doi.org/10.3390/jmse12122315 - 17 Dec 2024

Viewed by 494

Abstract

In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks the crucial significance of the spatial dependency relationships among trajectory points, posing challenges for comprehensively capturing the intricate features of vessel travel patterns. To address this limitation, our study introduces a novel multi-graph fusion representation method that integrates both trajectory sequences and dependency relationships to optimize the task of vessel type recognition. The proposed method initially extracts the spatiotemporal features and behavioral semantic features from vessel trajectories. By utilizing these behavioral semantic features, the key nodes within the trajectory that exhibit dependencies are identified. Subsequently, graph structures are constructed to represent the intricate dependencies between these nodes and the sequences of trajectory points. These graph structures are then processed through graph convolutional networks (GCNs), which integrate various sources of information within the graphs to obtain behavioral representations of vessel trajectories. Finally, these representations are applied to the task of vessel type recognition for experimental validation. The experimental results indicate that this method significantly enhances vessel type recognition performance when compared to other baseline methods. Additionally, ablation experiments have been conducted to validate the effectiveness of each component of the method. This innovative approach not only delves deeply into the behavioral representations of vessel trajectories but also contributes to advancements in intelligent water traffic control. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)

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24 pages, 6958 KiB

Open AccessArticle

Ship Trajectory Prediction in Complex Waterways Based on Transformer and Social Variational Autoencoder (SocialVAE)

by Pengyue Wang, Mingyang Pan, Zongying Liu, Shaoxi Li, Yuanlong Chen and Yang Wei

J. Mar. Sci. Eng. 2024, 12(12), 2233; https://doi.org/10.3390/jmse12122233 - 5 Dec 2024

Viewed by 732

Abstract

Ship trajectory prediction plays a key role in the early warning and safety of maritime traffic. It is a necessary assistant tool that can forecast a ship’s trajectory in a certain period to prevent ship collision. However, highly precise prediction of long-term ship trajectories is still a challenge. This study proposes a ship trajectory prediction model called ShipTrack-TVAE, which is based on a Variational Autoencoder (SocialVAE) and Transformer architecture. It aims to address ship trajectory prediction tasks in complex waterways. To enable the model to avoid potential collision risks, this study designs a collision avoidance mechanism, which comprehensively incorporates safety constraints related to the distance between ships into the loss function. The experimental results show that on the Qiongzhou Strait ship AIS dataset, the Average Displacement Error (ADE) of ShipTrack-TVAE improved by 21.85% compared to the current state-of-the-art trajectory prediction model, SocialVAE, while the Final Displacement Error (FDE) improved by 17.83%. The experimental results demonstrate that the ShipTrack-TVAE model can effectively improve the prediction accuracy of short-term, medium-term, and long-term ship trajectories. It has excellent performance and provides a certain reference value for advancing unmanned ship collision avoidance. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)

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21 pages, 2250 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1185

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 977

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 853

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 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

Open AccessArticle

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 2 | Viewed by 1041

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 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

Open AccessArticle

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 1366

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) 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

L_{t o k e n}

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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