Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.0
2.8
Journals
JMSE
Special Issues
Autonomous Ship and Harbor Maneuvering: Modeling and Control
share announcement

Autonomous Ship and Harbor Maneuvering: Modeling and Control

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: 30 April 2026 | Viewed by 3847

Special Issue Editors

Dr. Sewon Kim

E-Mail Website
Guest Editor
AI Robotics Department, Sejong University, Seoul, Republic of Korea
Interests: autonomous vessel; autonomous harbor; route optimization; ship and port interaction
Dr. Chungkuk Jin

E-Mail Website
Guest Editor
College of Engineering and Science, Department of Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL, USA
Interests: fluid-structure interaction; ocean renewable energy systems; marine infrastructures; hydro-elasticity; digital twin; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous ships and autonomous ports are fundamentally changing the way maritime logistics is operated. Autonomous ships and autonomous ports are emerging technologies that not only lead to independent technological developments for ships and ports but also to changes in the entire process of autonomous ships entering and leaving ports, including docking, loading, and unloading cargo. As these unprecedented technologies are introduced to maritime logistics, research on new technologies to improve operational safety and operational efficiency is essential. This Special Issue aims to publish research on novel operational methods, controller modeling, digital twins, and the utilization and interaction of artificial intelligence that is evolving with the introduction of autonomous ships and autonomous ports.

This Special Issue aims to publish cutting-edge research in these domains, ensuring rapid peer review and dissemination of high-quality studies for research and practical applications.

Dr. Sewon Kim
Dr. Chungkuk Jin
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

  • autonomous vessel arrival and departure
  • autonomous harbor operation method
  • autonomous vessel berthing and berth planning
  • autonomous vessel cargo loading and unloading
  • harbor autonomous guided vehicle and yard tractor scheduling
  • autonomous vessel digital twin
  • autonomous harbor digital twin
  • autonomous vessel and harbor interaction
  • ship and harbor data standardization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

30 pages, 4360 KB  
Article
Development of a Reinforcement Learning-Based Ship Voyage Planning Optimization Method Applying Machine Learning-Based Berth Dwell-Time Prediction as a Time Constraint
by Youngseo Park, Suhwan Kim, Jeongon Eom and Sewon Kim
J. Mar. Sci. Eng. 2026, 14(1), 43; https://doi.org/10.3390/jmse14010043 - 25 Dec 2025
Viewed by 1016
Abstract
Global container shipping faces increasing pressure to reduce fuel consumption and greenhouse gas (GHG) emissions while still meeting strict port schedules under highly uncertain terminal operations and met-ocean conditions. However, most existing voyage-planning approaches either ignore real port operation variability or treat fuel [...] Read more.
Global container shipping faces increasing pressure to reduce fuel consumption and greenhouse gas (GHG) emissions while still meeting strict port schedules under highly uncertain terminal operations and met-ocean conditions. However, most existing voyage-planning approaches either ignore real port operation variability or treat fuel optimization and just-in-time (JIT) arrival as separate problems, limiting their applicability in actual operations. This study presents a data-driven just-in-time voyage optimization framework that integrates port-side uncertainty and marine environmental dynamics into the routing process. A dwell-time prediction model based on Gradient Boosting was developed using port throughput and meteorological–oceanographic variables, achieving a validation accuracy of R2 = 0.84 and providing a data-driven required time of arrival (RTA) estimate. A Transformer encoder model was constructed to forecast fuel consumption from multivariate navigation and environmental data, and the model achieved a segment-level predictive performance with an R2 value of approximately 0.99. These predictive modules were embedded into a Deep Q-Network (DQN) routing model capable of optimizing headings and speed profiles under spatially varying ocean conditions. Experiments were conducted on three container-carrier routes in which the historical AIS trajectories served as operational benchmark routes. Compared with these AIS-based baselines, the optimized routes reduced fuel consumption and CO2 emissions by approximately 26% to 69%, while driving the JIT arrival deviation close to zero. The proposed framework provides a unified approach that links port operations, fuel dynamics, and ocean-aware route planning, offering practical benefits for smart and autonomous ship navigation. Full article
(This article belongs to the Special Issue Autonomous Ship and Harbor Maneuvering: Modeling and Control)
►▼ Show Figures

Figure 1

23 pages, 1469 KB  
Article
Wave Direction Classification for Advancing Ships Using Artificial Neural Networks Based on Motion Response Spectra
by Taehyun Yoon, Young Il Park, Won-Ju Lee and Jeong-Hwan Kim
J. Mar. Sci. Eng. 2026, 14(1), 6; https://doi.org/10.3390/jmse14010006 - 19 Dec 2025
Viewed by 769
Abstract
This study proposes a novel artificial neural network-based methodology for classifying the incident wave direction during ship navigation using the heave–roll–pitch motion response spectra as input. The proposed model demonstrated a balanced performance with an overall accuracy of approximately 0.888, effectively classifying the [...] Read more.
This study proposes a novel artificial neural network-based methodology for classifying the incident wave direction during ship navigation using the heave–roll–pitch motion response spectra as input. The proposed model demonstrated a balanced performance with an overall accuracy of approximately 0.888, effectively classifying the wave direction into three major categories: head-sea, beam-sea, and following-sea. The methodology utilizes Response Amplitude Operators derived from linear potential flow theory to generate motion response spectra, which are then used to classify the incident wave direction. The model effectively learns the frequency-distribution characteristics of the response spectrum, enabling wave direction classification without the need for complex inverse analysis procedures. This approach is significant in that it allows wave direction recognition solely based on measurable ship motion responses, without the need for additional external sensors or mathematical modeling. This data-driven approach has strong potential for integration into autonomous ship situational awareness modules and real-time wave monitoring technologies. However, the study simplified the directional domain into three representative groups, and the model was validated primarily using a numerically generated dataset, indicating the need for future improvements. Future research will expand the dataset to include a broader range of sea states, improve directional resolution, and explore continuous wave direction prediction. Additionally, further validation using field-measured data will be conducted to assess the real-time applicability of the proposed model. Full article
(This article belongs to the Special Issue Autonomous Ship and Harbor Maneuvering: Modeling and Control)
►▼ Show Figures

Figure 1

24 pages, 6057 KB  
Article
Numerical Analysis Comparison Between ANSYS AQWA and OrcaFlex for a Hollow Box-Shaped Floating Structure
by Se Hwan Park, Sang Gyu Cheon and Woo Chul Chung
J. Mar. Sci. Eng. 2025, 13(12), 2407; https://doi.org/10.3390/jmse13122407 - 18 Dec 2025
Viewed by 1340
Abstract
This study presents a numerical comparison between ANSYS AQWA (2023 R2) and the OrcaFlex package (OrcaWave + OrcaFlex) for a 10 × 10 × 2 m rectangular floating structure. The hydrodynamic coefficients and displacement/load RAOs obtained from the two solvers exhibit nearly identical [...] Read more.
This study presents a numerical comparison between ANSYS AQWA (2023 R2) and the OrcaFlex package (OrcaWave + OrcaFlex) for a 10 × 10 × 2 m rectangular floating structure. The hydrodynamic coefficients and displacement/load RAOs obtained from the two solvers exhibit nearly identical behavior, with deviations below 1% across all six motion modes. Under irregular wave conditions (Hs = 7 m, Tp = 8 s, 0° heading) and three mooring line lengths (145, 150, and 155 m), both solvers produced comparable mean surge motions and mean mooring tensions. However, OrcaFlex predicted 40–50% higher peak tensions due to its fully dynamic representation of slack–taut transitions and snap loading effects, whereas AQWA’s quasi-static catenary formulation filtered out these short-duration peaks. These findings confirm that although the two solvers are highly consistent in frequency-domain hydrodynamics, their time-domain predictions diverge when nonlinear mooring behavior becomes dominant. The study provides a transparent and reproducible benchmarking framework for cross-validation of potential-flow-based tools used in floating offshore structure design. Full article
(This article belongs to the Special Issue Autonomous Ship and Harbor Maneuvering: Modeling and Control)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 908 KB  
Review
Exploring Recent Maritime Research on AIS-Based Ship Behavior Analysis and Modeling
by Anila Duka, Houxiang Zhang, Pero Vidan and Guoyuan Li
J. Mar. Sci. Eng. 2026, 14(8), 712; https://doi.org/10.3390/jmse14080712 - 11 Apr 2026
Viewed by 113
Abstract
Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and [...] Read more.
Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article
(This article belongs to the Special Issue Autonomous Ship and Harbor Maneuvering: Modeling and Control)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop