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: 1 February 2026 | Viewed by 774

Special Issue Editors


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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
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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

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

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Keywords

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

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Published Papers (1 paper)

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Research

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
Viewed by 470
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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