Advances in Marine Autonomous Vehicles

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 June 2026 | Viewed by 1345

Special Issue Editors

Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05000, Republic of Korea
Interests: system dynamics; mechatronics; underwater vehicles; automation and robotics; trajectory tracking; path planning; multi-body dynamic modeling; intelligent navigation; autonomous vehicles
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Guest Editor
Division of Mechanical and Energy Systems Engineering, Korea Maritime and Ocean University, Busan, Republic of Korea
Interests: actuators; robotics; mechatronics; control theory; system dynamics modeling

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Guest Editor
Department of Ocean Advance Materialrs Convergence Engineering, Korea Maritime and Ocean University, Busan, Republic of Korea
Interests: ocean robotics; control system; ship dynamics

Special Issue Information

Dear Colleagues,

1. Background

Marine Autonomous Vehicles (MAVs)—including AUVs (Autonomous Underwater Vehicles), ASVs/USVs (Autonomous Surface Vehicles), and hybrid platforms—have emerged as a response to the inherent challenges of operating in the ocean: extreme pressure, low visibility, lack of GPS, and high economic/logistical costs.

Recent progress in key technologies has accelerated MAV development:

  • Advances in navigation and sensing (INS, DVL, multibeam sonar, SAS);
  • Improvements in energy systems (Li-ion batteries, fuel cells, energy harvesting);
  • Growth of AI, robotics, and autonomy algorithms;
  • Rising need for environmental monitoring, maritime security, and offshore industry automation.

2. Aim and Scope

Aim—Aims of MAV papers

  • To replace or augment human presence in dangerous or inaccessible marine environments;
  • To enable long-duration, cost-effective ocean exploration and monitoring;
  • To provide precise, autonomous sensing, mapping, and inspection;
  • To support maritime security, defense, and environmental protection;
  • To develop systems that can perceive, decide, and act autonomously with minimal human supervision.

Scope—Major Mission Areas

  1. Oceanographic research: physical, chemical, and biological data collection;
  2. Seafloor mapping and resource exploration: geology, mineral deposits, offshore wind site assessment;
  3. Military and security applications: mine detection, anti-submarine surveillance, infrastructure protection;
  4. Industrial operations: pipeline inspection, offshore infrastructure monitoring, underwater construction support;
  5. Environmental monitoring: HAB detection, greenhouse gas flux, pollution tracking;
  6. Disaster response and search and rescue: locating wrecks, assisting recovery operations.

3. Cutting-edge Research Area-AI-enabled Autonomy and Decision-making

  • Multi-vehicle coordination and swarm systems;
  • Long-endurance and energy innovations;
  • Advanced underwater communication;
  • High-resolution sensing and mapping;
  • Human–robot teaming and supervisory control.

Dr. Mai The Vu
Prof. Dr. Hyeung-Sik Choi
Prof. Dr. Joonyoung 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

  • marine autonomous vehicle
  • AI-enabled autonomy and decision-making
  • ocean exploration and monitoring
  • ocean data collection
  • underwater communication
  • maritime security

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Published Papers (1 paper)

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Research

17 pages, 3074 KB  
Article
Dual-Modal Vision–Sonar Object Detection for Underwater Robots Based on Deep Learning
by Xiaoming Wang, Zhenyu Wang and Dexue Bi
J. Mar. Sci. Eng. 2026, 14(4), 338; https://doi.org/10.3390/jmse14040338 - 10 Feb 2026
Viewed by 956
Abstract
Applying state-of-the-art RGB object detectors (e.g., YOLOv8) to underwater scenes often yields unstable performance due to scattering, absorption, illumination deficiency, and bandwidth-limited transmission that severely corrupt image contrast and details. Forward-looking sonar (FLS) remains informative in turbid or low-visibility water, yet its low [...] Read more.
Applying state-of-the-art RGB object detectors (e.g., YOLOv8) to underwater scenes often yields unstable performance due to scattering, absorption, illumination deficiency, and bandwidth-limited transmission that severely corrupt image contrast and details. Forward-looking sonar (FLS) remains informative in turbid or low-visibility water, yet its low resolution and weak semantics make conventional fusion architectures costly and difficult to deploy on resource-constrained robots. This paper proposes a paired-sample-free RGB–FLS joint training paradigm based on parameter sharing, where RGB and FLS images from different datasets are jointly used during training without any frame-level pairing or architectural modification. The resulting model preserves the original detector parameter scale and inference cost, and requires only RGB input at test time. Experiments on the SeaClear and Marine Debris FLS datasets under six representative underwater degradation factors (contrast loss, blur, resolution reduction, color cast, and JPEG compression) show consistent robustness gains over RGB-only training. In particular, under severe low-contrast corruption, the proposed training strategy improves mAP50 by more than 14 percentage points compared with the RGB-only baseline. These results indicate that sonar-domain supervision functions as an auxiliary structural constraint during optimization, rather than a conventional multi-source data enlargement. By forcing a shared-parameter detector to fit a texture-poor, geometry-dominant sonar domain, the learned representation is biased away from color/texture shortcuts and becomes more stable under adverse underwater degradations, without increasing deployment complexity. Full article
(This article belongs to the Special Issue Advances in Marine Autonomous Vehicles)
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