Advances in the Decision-Making and Control of Autonomous Marine 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: 10 April 2026 | Viewed by 1253

Special Issue Editors


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Guest Editor
Naval Postgraduate School, Monterey, CA 93943, USA
Interests: guidance; navigation and control of unmanned air; surface and underwater vehicles; cooperative control; systems dynamics and control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Computing and IT, University of Doha for Science and Technology, Doha Z68, Qatar
Interests: research areas: robotics & autonomous systems; machine learning; mission-motion planning; situation awareness & decision making; AI

Special Issue Information

Dear Colleagues,

In recent years, autonomous marine vehicles (AMVs) have emerged as key enablers in a wide range of maritime applications, including environmental monitoring, seabed mapping, surveillance, search and rescue, sampling, and offshore infrastructure inspection. The increasing demand for long-duration missions in dynamic, uncertain, and GPS-denied environments has driven significant research and development in the areas of intelligent autonomy and real-time decision-making.

Despite notable progress, numerous technical challenges remain, particularly in achieving fully autonomous, persistent, and robust operations with minimal or no human intervention. These challenges include dynamic mission planning, fault tolerance, adaptation to unstructured environments, and safe interactions with other agents or infrastructure.

This Special Issue aims to present recent advances and pioneering research regarding the design, development, and implementation of decision-making, planning, and control systems for AMVs. The scope of this Special Issue includes a broad range of autonomous systems such as autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), and hybrid or multi-domain marine robots capable of operating across surface, underwater, or aerial environments.

We invite high-quality original research articles and review papers in, but not limited to, the following areas:

  • Autonomous mission planning and dynamic replanning;
  • Multi-vehicle task assignment and coordination;
  • Guidance, path planning, and trajectory optimization;
  • Motion planning and robust/adaptive control under uncertainty;
  • AI/ML-based decision-making frameworks;
  • Fault detection, resilience, and safe operation in harsh environments;
  • Human-in-the-loop autonomy and shared control architectures;
  • Real-time situational awareness and perception-driven control.

Dr. Amirmehdi Yazdani
Prof. Dr. Oleg Yakimenko
Dr. Somaiyeh MahmoudZadeh
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 monthly 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 marine vehicles
  • autonomous underwater vehicles
  • autonomous surface vehicles
  • intelligent control
  • marine robotics
  • path planning
  • decision-making
  • AI for marine systems
  • multi-agent coordination
  • resilient autonomy

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Published Papers (2 papers)

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Research

31 pages, 1040 KB  
Article
Navigating the Trade-Offs: A Quantitative Analysis of Reinforcement Learning Reward Functions for Autonomous Maritime Collision Avoidance
by Björn Krautwig, Dominik Wans, Li Li, Till Temmen, Lucas Koch, Markus Eisenbarth and Jakob Andert
J. Mar. Sci. Eng. 2025, 13(12), 2233; https://doi.org/10.3390/jmse13122233 - 23 Nov 2025
Viewed by 372
Abstract
Autonomous navigation is critical for unlocking the full potential of Unmanned Surface Vehicles (USVs) in complex maritime environments. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for developing self-learning control policies, yet the design of reward functions to balance conflicting objectives, [...] Read more.
Autonomous navigation is critical for unlocking the full potential of Unmanned Surface Vehicles (USVs) in complex maritime environments. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for developing self-learning control policies, yet the design of reward functions to balance conflicting objectives, particularly fast arrival at the target position and collision avoidance, remains a major challenge. The precise, quantitative impact of reward parameterization on a USV’s maneuvering behavior and the inherent performance trade-offs have not been thoroughly investigated. Here, we demonstrate that by systematically varying reward function weights within a framework relying on the Proximal Policy Optimization (PPO), it is possible to quantitatively map the trade-off between collision avoidance safety and mission time. Our results, derived from simulations, show that agents trained with balanced reward weights achieve target-reaching success rates exceeding 98% in dynamic multi-obstacle scenarios. Conversely, configurations that disproportionately penalize obstacle proximity lead to overly cautious behavior and mission failure, with success rates dropping to 22% due to workspace boundary violations. This work provides a data-driven methodological framework for reward function design and parameter selection in safety-critical robotic applications, moving beyond ad-hoc tuning towards a more structured parameter influence analysis. Full article
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30 pages, 19955 KB  
Article
Adaptive Sampling of Marine Submesoscale Features Using Gaussian Process Regression with Unmanned Platforms
by Wenbo Wang, Haibo Tang, Wei Song, Shuangshuang Fan and Dongxiao Wang
J. Mar. Sci. Eng. 2025, 13(11), 2088; https://doi.org/10.3390/jmse13112088 - 3 Nov 2025
Viewed by 407
Abstract
Submesoscale processes, characterized by strong vertical velocities that generate sea surface temperature (SST) fronts as well as O(1) Rossby number (Ro), are critical to ocean mixing and biogeochemical transport, yet their observation is hampered by cost and spatial limitations. Hence, this study [...] Read more.
Submesoscale processes, characterized by strong vertical velocities that generate sea surface temperature (SST) fronts as well as O(1) Rossby number (Ro), are critical to ocean mixing and biogeochemical transport, yet their observation is hampered by cost and spatial limitations. Hence, this study proposes an adaptive sampling framework for unmanned surface vehicles (USVs) that integrates Gaussian process regression (GPR) with submesoscale physical characteristics for efficient, targeted sampling. Three composite-kernel GPR models are developed to predict SST, zonal velocity U, and meridional velocity V, providing predictive fields to support adaptive path planning. A robust coupled gradient indicator (CGI) is further introduced to identify SST frontal zones, where the maximum CGI values are used to select candidate waypoints. Connecting these waypoints yields adaptive paths aligned with frontal structures, while a Ro threshold (0.5–2) automatically triggers spiral-intensive sampling to collect more useful data. Simulation results show that the planned paths effectively capture SST gradient and submesoscale dynamics. The final environment reconstruction achieved the desired accuracy after model retraining, and deployment analysis informs optimal platform deployment. Overall, the proposed framework couples environmental prediction, adaptive path planning, and intelligent sampling, offering an effective strategy for advancing the observation of submesoscale ocean processes. Full article
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