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Autonomous Ship and Harbor Maneuvering: Modeling and Control
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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 728

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
Department of Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL, USA
Interests: route optimization; offshore platform motion
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

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Published Papers (2 papers)

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Research

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 141
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)
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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 248
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)
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