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Advanced Research on Path Planning for Intelligent Ships
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Advanced Research on Path Planning for Intelligent 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: 20 October 2026 | Viewed by 3842

Special Issue Editors

Prof. Dr. Joo-Sung Kim

E-Mail Website
Guest Editor
Division of Navigation Science, Mokpo National Maritime University, Mokpo 58628, Republic of Korea
Interests: machine learning; anomaly detection; artificial intelligence; collision avoidance; path planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ho Namgung

E-Mail Website
Guest Editor
Department of Maritime Industry Convergence, Mokpo National Maritime University, Mokpo 58628, Republic of Korea
Interests: collision avoidance; fuzzy inference system; advanced machine learning; artificial intelligence; maritime autonomous surface ships; local route planning; information exchange
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kwang-il Kim

E-Mail Website
Guest Editor
Department of Marine Industry and Maritime Police, Jeju National University, Jeju 63243, Republic of Korea
Interests: ship transportation; deep learning; maritime artificial intelligent; maritime big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to present the latest advancements in intelligent path planning for Maritime Autonomous Surface Ships (MASS). With rapid developments in artificial intelligence, machine learning, and sensor technologies, robust and reliable path planning has become a core component of safe and efficient autonomous navigation.

We invite original research and review articles on innovative methodologies, including collision avoidance algorithms, fuzzy inference systems, deep learning-based models, anomaly detection, and local/global route planning techniques. Submissions focused on real-time decision-making, simulation frameworks, VTS coordination, and data-driven approaches for maritime environments are particularly welcome.

Through this Special Issue, we seek to foster interdisciplinary collaboration and offer a platform on which maritime researchers and engineers may share breakthroughs that will shape the future of intelligent ship navigation.

Prof. Dr. Joo-Sung Kim
Prof. Dr. Ho Namgung
Prof. Dr. Kwang-il Kim
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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • maritime autonomous surface ships (MASS)
  • path planning
  • collision avoidance
  • artificial intelligence
  • machine learning

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Published Papers (5 papers)

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Research

45 pages, 5461 KB  
Article
Integrated Analysis of NOx Reduction and Performance Enhancement in HYUNDAI-HiMSEN 7H35DFP Dual-Fuel Marine Engine
by Kwang-Sik Jo, Sang-Gon Cho and Seung-Hun Han
J. Mar. Sci. Eng. 2026, 14(4), 349; https://doi.org/10.3390/jmse14040349 - 11 Feb 2026
Cited by 1 | Viewed by 724
Abstract
This comprehensive study presents an integrated analysis of NOx reduction strategies and operational optimization for the HYUNDAI-HiMSEN 7H35DFP dual-fuel marine engine. The optimization scope focuses on selective catalytic reduction control strategies and operational decision-making (fuel mode selection, load management) rather than engine [...] Read more.
This comprehensive study presents an integrated analysis of NOx reduction strategies and operational optimization for the HYUNDAI-HiMSEN 7H35DFP dual-fuel marine engine. The optimization scope focuses on selective catalytic reduction control strategies and operational decision-making (fuel mode selection, load management) rather than engine hardware modifications, ensuring practical applicability within certified marine engine operational envelopes. The research employs a multifaceted approach combining experimental investigation, computational fluid dynamics (CFD) modeling, and advanced control algorithms to address the stringent IMO Tier III emission standards. The 3500 kW, 7-cylinder engine achieves IMO Tier III compliance through dual pathways: (1) gas mode operation meeting the 2.4 g/kWh limit inherently with measured emissions of 1.41–2.29 g/kWh across 25–100% load without aftertreatment, and (2) diesel mode achieving compliance via SCR aftertreatment, reducing Tier II baseline emissions (7.68–10.71 g/kWh) by 75–82% to final values of 1.60–1.96 g/kWh. The research quantifies NOx reduction mechanisms separately for each operating mode and establishes optimal operational strategies for mode selection. A MATLAB v2025a-based SCR optimization model successfully predicts optimal urea injection rates, achieving >75% NOx reduction efficiency across all operating conditions. Multivariate analysis using principal component analysis identifies the following three primary factors explaining 89.3% of dataset variability: combustion intensity (45.2%), fuel mixing characteristics (28.7%), and thermal management (15.4%). CFD analysis reveals that gas mode combustion produces more uniform temperature distributions (peak ~2000 K) compared to diesel operation (>2200 K), directly explaining NOx generation differences. The developed digital twin framework with machine learning algorithms achieves 94.2% accuracy in SCR catalyst degradation prediction and 91.8% in fuel injection system performance prediction. Waste heat recovery analysis indicates 25–30% of fuel energy resides in exhaust gases, with theoretical energy recovery potential of 8.5–15.3%. This integrated approach validates dual-fuel technology’s capability to meet current and future maritime environmental regulations while maintaining operational flexibility. Full article
(This article belongs to the Special Issue Advanced Research on Path Planning for Intelligent Ships)
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23 pages, 5241 KB  
Article
BAARTR: Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction from Sparse AIS
by Hee-jong Choi, Joo-sung Kim and Dae-han Lee
J. Mar. Sci. Eng. 2026, 14(2), 116; https://doi.org/10.3390/jmse14020116 - 7 Jan 2026
Viewed by 500
Abstract
The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel [...] Read more.
The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity (O(N)). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article
(This article belongs to the Special Issue Advanced Research on Path Planning for Intelligent Ships)
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27 pages, 4812 KB  
Article
Development of an Initial Burial Rate Estimation Simulator for Bottom-Contact Mines and a Reinforcement Learning-Based Mine-Laying Route Optimization Method
by Su Hwan Kim, Young Seo Park and Se Won Kim
J. Mar. Sci. Eng. 2026, 14(1), 51; https://doi.org/10.3390/jmse14010051 - 26 Dec 2025
Viewed by 494
Abstract
In modern naval operations, the strategic value of naval mines has been increasingly emphasized, highlighting the need for intelligent and efficient deployment strategies. This study proposes integrated framework that combines mine burial rate estimation with reinforcement learning-based optimization to generate mine-laying routes that [...] Read more.
In modern naval operations, the strategic value of naval mines has been increasingly emphasized, highlighting the need for intelligent and efficient deployment strategies. This study proposes integrated framework that combines mine burial rate estimation with reinforcement learning-based optimization to generate mine-laying routes that maximize burial effectiveness. An initial burial rate estimation simulator was developed using environmental factors such as sediment bulk density and shear strength estimated from sediment type and mean grain size to predict the burial rates of bottom-contact mines. The simulator was integrated into reinforcement learning frameworks—Deep Q-Network (DQN), and proximal policy optimization (PPO). The reinforcement learning methods were trained to autonomously explore the environment and generate routes that strategically utilize high burial regions while satisfying navigational constraints. Experimental results demonstrate that the reinforcement learning methods consistently generated routes with higher average burial rates while requiring significantly shorter computation time compared with the A* algorithm. These findings suggest that reinforcement learning, when coupled with environmental modeling, provides a practical and scalable strategy for improving the effectiveness, concealment, and autonomy of naval mine-laying operations. Full article
(This article belongs to the Special Issue Advanced Research on Path Planning for Intelligent Ships)
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22 pages, 12700 KB  
Article
An Adaptive Path Planning Algorithm for USV in Complex Waterways: SA-Bi-APF-RRT*
by Sixian Li, Ke Chen, Dongfang Li, Jieyu Xian, Tieli Lyu, Yimeng Li, Hong Zhu and Maohua Xiao
J. Mar. Sci. Eng. 2026, 14(1), 45; https://doi.org/10.3390/jmse14010045 - 25 Dec 2025
Viewed by 759
Abstract
In recent years, the RRT* algorithm has been widely applied in industrial fields because of its asymptotic optimality. However, the traditional RRT* algorithm exhibits limitations in terms of convergence speed and quality of generated paths, and its path exploration capability in complex environments [...] Read more.
In recent years, the RRT* algorithm has been widely applied in industrial fields because of its asymptotic optimality. However, the traditional RRT* algorithm exhibits limitations in terms of convergence speed and quality of generated paths, and its path exploration capability in complex environments remains inadequate. To address these issues, this study proposes a self-adaptive bidirectional APF-RRT* (SA-Bi-APF-RRT*) algorithm. Specifically, a hierarchical node expansion mechanism is established, enabling dynamic adjustment of the new node expansion strategy. Furthermore, a bidirectional artificial potential field (APF) guidance strategy is introduced to enhance obstacle avoidance performance. An obstacle range density evaluation module, which autonomously adjusts APF parameters according to the density distribution of surrounding obstacles, is then incorporated. Additionally, the algorithm integrates a segmented greedy approach with Bézier curve fitting techniques to achieve simultaneous optimization of path length and smoothness, while ensuring path safety. Finally, the proposed algorithm is compared against RRT*, GB-RRT*, Bi-RRT*, APF-RRT*, and Bi-APF-RRT*, demonstrating superior adaptability and efficiency in environments characterized by low iteration counts and high obstacle density. Results indicate that the SA-Bi-APF-RRT* algorithm constitutes a promising optimization solution for USVs path planning tasks. Full article
(This article belongs to the Special Issue Advanced Research on Path Planning for Intelligent Ships)
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19 pages, 8168 KB  
Article
Data-Driven Optimization of Ship Propulsion Efficiency and Emissions Considering Relative Wind
by Sang-A Park, Min-A Je, Suk-Ho Jung and Deuk-Jin Park
J. Mar. Sci. Eng. 2025, 13(11), 2120; https://doi.org/10.3390/jmse13112120 - 9 Nov 2025
Cited by 1 | Viewed by 779
Abstract
The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of [...] Read more.
The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of relative wind on ship propulsion efficiency and pollutant emissions. The collected navigational, engine performance, and emission data—including parameters such as shaft power, engine load, specific fuel oil consumption (SFOC), and NOx and SOx concentrations—were synchronized and then analyzed using statistical methods and a generalized additive model (GAM). Statistical correlation analysis and a GAM were applied to capture nonlinear relationships between variables. Compared with linear models, the GAM achieved higher predictive accuracy (R2 = 0.98) and effectively identified threshold and interaction effects. The results showed that headwind conditions increased the engine load by ~12% and SFOC by 8.4 g/kWh while tailwind conditions reduced SFOC by up to 6.7 g/kWh. NOx emissions peaked under headwind conditions and exhibited nonlinear escalation beyond a relative wind speed of 12 kn. An operational window was identified for simultaneous improvement of the propulsion efficiency and reduction in pollutant emissions under beam wind and tailwind conditions at moderate relative wind speeds of 6–10 kn and an engine load of 30–40%. These findings can serve as a guide for incorporating relative wind into operational strategies for maritime autonomous surface ships. Full article
(This article belongs to the Special Issue Advanced Research on Path Planning for Intelligent Ships)
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