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Advanced Control Strategies for Autonomous Maritime Systems
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Advanced Control Strategies for Autonomous Maritime Systems

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: closed (30 September 2025) | Viewed by 15252

Special Issue Editors

Dr. Hossein Nejatbakhsh Esfahani

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Clemson University, Clemson, SC, USA
Interests: robust/optimal control systems; safety-critical control; model predictive control; reinforcement learning; autonomous and intelligent platforms (unmanned aerial and marine vehicles)
Dr. Arash Bahari Kordabad

E-Mail Website
Guest Editor
Max Planck Institute for Software Systems, 67663 Kaiserslautern, Germany
Interests: safe reinforcement learning; model predictive control; optimization for autonomous vehicles; smart-grid applications; multi-agent systems
Prof. Dr. David Moreno-Salinas

E-Mail Website
Guest Editor
Department of Computer Science and Automatic Control, National Distance Education University (UNED), 28040 Madrid, Spain
Interests: localization; control; sensor networks; marine vehicles; identification and modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous Maritime Systems (AMS) are strongly connected to the maritime industry, with applications ranging from unmanned surface and underwater vehicles to intelligent shipping solutions. Advanced path planning and control algorithms are vital for ensuring that these unmanned vehicles can operate autonomously with high reliability and precision, especially in challenging maritime environments. These control approaches incorporate various elements of adaptive and robust machine learning methods and optimal control schemes, which allow AMS to safely navigate in the presence of external disturbances such as waves, ocean currents, and wind. By using advanced learning-based optimal control strategies, AMS can optimize their trajectory, minimize fuel consumption, and improve overall operational efficiency. These control methods also account for the inherent uncertainties in the maritime domain, ensuring system robustness against disturbances and equipment failures. In addition to basic navigation and collision avoidance, advanced control strategies enable higher-level autonomy for networked autonomous marine vehicles. For example, multi-agent systems coordination allows fleets of autonomous ships or underwater vehicles to collaborate on complex missions. This Special Issue offers a collection of high-quality research articles contributing to topics on:

  • Guidance, navigation, and control of autonomous surface and underwater vehicles;
  • Path following, path planning, and collision avoidance algorithms;
  • Methods and tools for the development of digital twins of marine control systems;
  • Risk-aware decision-making and safety-critical control of autonomous marine systems;
  • Intelligent autonomous marine systems;
  • Learning-based control algorithms for marine robotics;
  • Robust, adaptive, and nonlinear control approaches in marine systems;
  • Model Predictive Control (MPC) for motion planning and control of autonomous marine vehicles;
  • Distributed and cooperative control systems for marine operations.

Dr. Hossein Nejatbakhsh Esfahani
Dr. Arash Bahari Kordabad
Prof. Dr. David Moreno-Salinas
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

  • unmanned surface and underwater vehicles
  • guidance, navigation and control (GNC) algorithms
  • path planning and collision avoidance
  • robust and adaptive nonlinear control
  • intelligent and learning-based control
  • reinforcement learning methods and optimal control
  • advanced model predictive control schemes
  • cooperative and networked control
  • risk-aware and safety-critical control

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

Published Papers (13 papers)

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Research

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28 pages, 2910 KB  
Article
Estimation of Vessel Collision Risk Under Uncertainty Using Interval Type-2 Fuzzy Inference Systems and Dempster–Shafer Evidence Theory
by Jinwan Park
J. Mar. Sci. Eng. 2026, 14(1), 34; https://doi.org/10.3390/jmse14010034 - 24 Dec 2025
Viewed by 269
Abstract
This study proposes a collision-risk assessment framework that combines an interval type-2 fuzzy inference system with Dempster–Shafer evidence theory to more reliably evaluate vessel collision risk under the uncertainty inherent in AIS-based marine navigation data. The fuzzy system models membership-function uncertainty through a [...] Read more.
This study proposes a collision-risk assessment framework that combines an interval type-2 fuzzy inference system with Dempster–Shafer evidence theory to more reliably evaluate vessel collision risk under the uncertainty inherent in AIS-based marine navigation data. The fuzzy system models membership-function uncertainty through a footprint of uncertainty and produces time-indexed basic probability assignments that are subsequently combined through a Dempster–Shafer–based temporal integration process. Robust combination rules are incorporated to mitigate the counterintuitive results often produced by classical evidence combination. Furthermore, Lenart’s time-based criterion and Fujii’s spatial safety domain are unified to construct a three-level risk labeling scheme, overcoming the limitations of conventional binary risk classification. Case studies using real AIS data demonstrate improved predictive accuracy and significantly reduced uncertainty, particularly when using the robust symmetric combination rule. Overall, the proposed framework provides a systematic approach for handling structural uncertainty in maritime environments and supports more reliable collision-risk prediction and safer navigational decision-making. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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21 pages, 1500 KB  
Article
Intelligent Multi-Objective Path Planning for Unmanned Surface Vehicles via Deep and Fuzzy Reinforcement Learning
by Ioannis A. Bartsiokas, Charis Ntakolia, George Avdikos and Dimitris Lyridis
J. Mar. Sci. Eng. 2025, 13(12), 2285; https://doi.org/10.3390/jmse13122285 - 30 Nov 2025
Viewed by 547
Abstract
Unmanned Surface Vehicles (USVs) are increasingly employed in maritime operations requiring high levels of autonomy, safety, and energy efficiency. However, traditional path planning techniques struggle to simultaneously address multiple conflicting objectives such as fuel consumption, trajectory smoothness, and obstacle avoidance in dynamic maritime [...] Read more.
Unmanned Surface Vehicles (USVs) are increasingly employed in maritime operations requiring high levels of autonomy, safety, and energy efficiency. However, traditional path planning techniques struggle to simultaneously address multiple conflicting objectives such as fuel consumption, trajectory smoothness, and obstacle avoidance in dynamic maritime environments. To overcome these limitations, this paper introduces a Deep Q-Learning (DQN) framework and a novel Fuzzy Deep Q-Learning (F-DQN) algorithm that integrates Mamdani-type fuzzy reasoning into the reinforcement-learning (RL) reward model. The key contribution of the proposed approach lies in combining fuzzy inference with deep reinforcement learning (DRL) to achieve adaptive, interpretable, and multi-objective USV navigation—overcoming the fixed-weight reward limitations of existing DRL methods. The study develops a multi-objective reward formulation that jointly considers path deviation, curvature smoothness, and fuel consumption, and evaluates both algorithms in a simulation environment with varying obstacle densities. The results demonstrate that the proposed F-DQN model significantly improves trajectory optimality, convergence stability, and energy efficiency, achieving over 35% reduction in path length and approximately 70–80% lower fuel consumption compared with the baseline DQN, while maintaining comparable success rates. Overall, the findings highlight the effectiveness of fuzzy-augmented reinforcement learning in enabling efficient and interpretable autonomous maritime navigation. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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18 pages, 1788 KB  
Article
Robust Relative Space Motion Control of Underwater Vehicles Using Time Delay Estimation
by Gun Rae Cho, Hyungjoo Kang, Min-Gyu Kim, Sungho Park, Chulhee Bae, Han-Sol Jin, Seongho Jin and Ji-Hong Li
J. Mar. Sci. Eng. 2025, 13(11), 2214; https://doi.org/10.3390/jmse13112214 - 20 Nov 2025
Viewed by 414
Abstract
This paper presents a robust trajectory-tracking control framework for underwater vehicles operating in a relative coordinate system. Unlike conventional methods that define trajectories in the world frame, the proposed approach formulates the control problem directly in a moving reference frame, enabling accurate motion [...] Read more.
This paper presents a robust trajectory-tracking control framework for underwater vehicles operating in a relative coordinate system. Unlike conventional methods that define trajectories in the world frame, the proposed approach formulates the control problem directly in a moving reference frame, enabling accurate motion control with respect to dynamic and drifting objects affected by environmental disturbances such as ocean currents and waves. This relative-space formulation is particularly advantageous for tasks including diver guidance, floating-object inspection, and docking, where the reference itself is nonstationary. A coordinate transformation is introduced to consistently express the vehicle dynamics in the relative frame. Based on the transformed dynamics, a Time Delay Control (TDC) law is applied to estimate unmodeled dynamics and external disturbances without requiring precise system parameters. Theoretical stability analysis shows that the stability condition of the proposed controller is consistent with that of conventional TDC, allowing similar gain-tuning procedures. Simulation results demonstrate that the proposed controller achieves robust and smooth trajectory tracking even when the reference frame undergoes motion induced by ocean currents. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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28 pages, 16333 KB  
Article
Autonomous Navigation Control and Collision Avoidance Decision-Making of an Under-Actuated ASV Based on Deep Reinforcement Learning
by Yiting Wang, Zhiyao Li, Lei Wang and Xuefeng Wang
J. Mar. Sci. Eng. 2025, 13(11), 2108; https://doi.org/10.3390/jmse13112108 - 6 Nov 2025
Viewed by 924
Abstract
For efficient and safe navigation for an autonomous surface vehicle (ASV), this paper proposes an autonomous navigation behavior framework that integrates deep reinforcement learning (DRL) to achieve autonomous decision-making and low-level control actions in path following and collision avoidance. By controlling both the [...] Read more.
For efficient and safe navigation for an autonomous surface vehicle (ASV), this paper proposes an autonomous navigation behavior framework that integrates deep reinforcement learning (DRL) to achieve autonomous decision-making and low-level control actions in path following and collision avoidance. By controlling both the propeller speed and the rudder angle, the policy of each behavior pattern is trained with the soft actor–critic (SAC) algorithm. Moreover, a dynamic obstacle trajectory predictor based on the Kalman filter and the long short-term memory module is developed for obstacle avoidance. Simulations and physical experiments using an under-actuated very large crude carrier (VLCC) model indicate that our DRL-based method produces appreciable performance gains in ASV autonomous navigation under environmental disturbances, which enables forecasting of the expected state of a vessel over a future time and improves the operational efficiency of the navigation process. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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26 pages, 4340 KB  
Article
Vertical Motion Stabilization of High-Speed Multihulls in Irregular Seas Using ESO-Based Backstepping Control
by Xianjin Fang, Huayang Li, Zhilin Liu, Guosheng Li, Tianze Ni, Fan Jiang and Jie Zhang
J. Mar. Sci. Eng. 2025, 13(11), 2040; https://doi.org/10.3390/jmse13112040 - 24 Oct 2025
Viewed by 396
Abstract
The severe vertical motion of high-speed multihull vessels significantly impairs their seakeeping performance, making the design of effective anti-motion controllers crucial. However, existing controllers, predominantly designed based on deterministic dynamic models, suffer from limitations such as insufficient robustness, reliance on empirical knowledge, structural [...] Read more.
The severe vertical motion of high-speed multihull vessels significantly impairs their seakeeping performance, making the design of effective anti-motion controllers crucial. However, existing controllers, predominantly designed based on deterministic dynamic models, suffer from limitations such as insufficient robustness, reliance on empirical knowledge, structural complexity, and suboptimal performance, which hinder their practical applicability. To address this, this paper proposes a robust decoupled vertical motion controller based on the step response inversion method and incorporating an Extended State Observer (ESO) uncertainty compensation term. The control algorithm is designed leveraging the equivalent noise bandwidth theory to account for the stochastic characteristics of pitch/heave motion, with ESO compensation introduced to enhance robustness. The stability of the closed loop system is rigorously proven through theoretical analysis. Simulation results demonstrate that the proposed algorithm significantly suppresses the amplitudes of both pitch and heave motions. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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35 pages, 3129 KB  
Article
Spatiotemporal Meta-Reinforcement Learning for Multi-USV Adversarial Games Using a Hybrid GAT-Transformer
by Yang Xiong, Shangwen Wang, Hongjun Tian, Guijie Liu, Zihao Shan, Yijie Yin, Jun Tao, Haonan Ye and Ying Tang
J. Mar. Sci. Eng. 2025, 13(8), 1593; https://doi.org/10.3390/jmse13081593 - 20 Aug 2025
Cited by 1 | Viewed by 1389
Abstract
Coordinating Multi-Unmanned Surface Vehicle (USV) swarms in complex, adversarial maritime environments is a significant challenge, as existing multi-agent reinforcement learning (MARL) methods often fail to capture intricate spatiotemporal dependencies, leading to suboptimal policies. To address this, we propose Adv-TransAC, a novel Spatio-Temporal Meta-Reinforcement [...] Read more.
Coordinating Multi-Unmanned Surface Vehicle (USV) swarms in complex, adversarial maritime environments is a significant challenge, as existing multi-agent reinforcement learning (MARL) methods often fail to capture intricate spatiotemporal dependencies, leading to suboptimal policies. To address this, we propose Adv-TransAC, a novel Spatio-Temporal Meta-Reinforcement Learning framework. Its core innovation is a hybrid GAT-transformer architecture that decouples spatial and temporal reasoning: a Graph Attention Network (GAT) models instantaneous tactical formations, while a transformer analyzes their temporal evolution to infer intent. This is combined with an adversarial meta-learning mechanism to enable rapid adaptation to opponent tactics. In high-fidelity escort and defense simulations, Adv-TransAC significantly outperforms state-of-the-art MARL baselines in task success rate and policy robustness. The learned policies demonstrate the emergence of complex cooperative behaviors, such as intelligent risk-aware coordination and proactive interception maneuvers. The framework’s practicality is further validated by a communication-efficient federated optimization architecture. By effectively modeling spatiotemporal dynamics and enabling rapid adaptation, Adv-TransAC provides a powerful solution that moves beyond reactive decision-making, establishing a strong foundation for next-generation, intelligent maritime platforms. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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22 pages, 5254 KB  
Article
Exploring Simulation Methods to Counter Cyber-Attacks on the Steering Systems of the Maritime Autonomous Surface Ship (MASS)
by Igor Astrov, Sanja Bauk and Pentti Kujala
J. Mar. Sci. Eng. 2025, 13(8), 1470; https://doi.org/10.3390/jmse13081470 - 31 Jul 2025
Viewed by 1252
Abstract
This paper presents a simulation-based investigation into control strategies for mitigating the consequences of cyber-assault on the steering systems of the Maritime Autonomous Surface Ships (MASS). The study focuses on two simulation experiments conducted within the Simulink/MATLAB environment, utilizing the catamaran “Nymo” MASS [...] Read more.
This paper presents a simulation-based investigation into control strategies for mitigating the consequences of cyber-assault on the steering systems of the Maritime Autonomous Surface Ships (MASS). The study focuses on two simulation experiments conducted within the Simulink/MATLAB environment, utilizing the catamaran “Nymo” MASS mathematical model to represent vessel dynamics. Cyber-attacks are modeled as external disturbances affecting the rudder control signal, emulating realistic interference scenarios. To assess control resilience, two configurations are compared during a representative turning maneuver to a specified heading: (1) a Proportional–Integral–Derivative (PID) regulator augmented with a Least Mean Squares (LMS) adaptive filter, and (2) a Nonlinear Autoregressive Moving Average with Exogenous Input (NARMA-L2) neural network regulator. The PID and LMS configurations aim to enhance the disturbance rejection capabilities of the classical controller through adaptive filtering, while the NARMA-L2 approach represents a data-driven, nonlinear control alternative. Simulation results indicate that although the PID and LMS setups demonstrate improved performance over standalone PID in the presence of cyber-induced disturbances, the NARMA-L2 controller exhibits superior adaptability, accuracy, and robustness under adversarial conditions. These findings suggest that neural network-based control offers a promising pathway for developing cyber-resilient steering systems in autonomous maritime vessels. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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20 pages, 1076 KB  
Article
Passivity-Based Sliding Mode Control for the Robust Trajectory Tracking of Unmanned Surface Vessels Under External Disturbances and Model Uncertainty
by Luke Ma, Siyi Pang, Yao He, Yongxin Wu, Yanjun Li and Weijun Zhou
J. Mar. Sci. Eng. 2025, 13(2), 364; https://doi.org/10.3390/jmse13020364 - 16 Feb 2025
Viewed by 1284
Abstract
This study uses a port-Hamiltonian framework to address trajectory tracking control for unmanned surface vessels (USVs) under unknown disturbances. A passivity-based sliding mode controller is designed, integrating adaptive disturbance estimation and an RBFNN-based uncertainty estimator. Stability is rigorously proven, and simulations confirm superior [...] Read more.
This study uses a port-Hamiltonian framework to address trajectory tracking control for unmanned surface vessels (USVs) under unknown disturbances. A passivity-based sliding mode controller is designed, integrating adaptive disturbance estimation and an RBFNN-based uncertainty estimator. Stability is rigorously proven, and simulations confirm superior tracking performance, strong disturbance rejection, and accurate uncertainty estimation. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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21 pages, 2523 KB  
Article
Networked Predictive Trajectory Tracking Control for Underactuated USV with Time-Varying Delays
by Tao Lei, Yuanqiao Wen, Yi Yu, Minglong Zhang, Xin Xiong and Kang Tian
J. Mar. Sci. Eng. 2025, 13(1), 132; https://doi.org/10.3390/jmse13010132 - 13 Jan 2025
Cited by 3 | Viewed by 1494
Abstract
This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding [...] Read more.
This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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16 pages, 2102 KB  
Article
Advanced Control for Shipboard Cranes with Asymmetric Output Constraints
by Mingxuan Cao, Meng Xu, Yongqiao Gao, Tianlei Wang, Anan Deng and Zhenyu Liu
J. Mar. Sci. Eng. 2025, 13(1), 91; https://doi.org/10.3390/jmse13010091 - 6 Jan 2025
Cited by 2 | Viewed by 1144
Abstract
Considering the anti-swing control and output constraint problems of shipboard cranes, a nonlinear anti-swing controller based on asymmetric barrier Lyapunov functions (BLFs) is designed. First, model transformation mitigates the explicit effects of ship roll on the desired position and payload fluctuations. Then, a [...] Read more.
Considering the anti-swing control and output constraint problems of shipboard cranes, a nonlinear anti-swing controller based on asymmetric barrier Lyapunov functions (BLFs) is designed. First, model transformation mitigates the explicit effects of ship roll on the desired position and payload fluctuations. Then, a newly constructed BLF is introduced into the energy-based Lyapunov candidate function to generate nonlinear displacement and angle constraint terms to control the rope length and boom luffing angle. Among these, constraints with positive bounds are effectively handled by the proposed BLF. For the swing constraints of the unactuated payload, a carefully designed relevant constraint term is embedded in the controller by constructing an auxiliary signal, and strict theoretical analysis is provided by using a reductio ad absurdum argument. Additionally, the auxiliary signal effectively couples the boom and payload motions, thereby improving swing suppression performance. Finally, the asymptotic stability is proven using LaSalle’s invariance principle. The simulation comparison results indicate that the proposed method exhibits satisfactory performance in swing suppression control and output constraints. In all simulation cases, the payload swing angle complies with the 3° constraint and converges to the desired range within 6 s. This study provides an effective solution to the control challenges of shipboard crane systems operating in confined spaces, offering significant practical value and applicability. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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25 pages, 4284 KB  
Article
Reliable, Energy-Optimized, and Void-Aware (REOVA), Routing Protocol with Strategic Deployment in Mobile Underwater Acoustic Communications
by Muhammad Umar Khan, Muhammad Aamir and Pablo Otero
J. Mar. Sci. Eng. 2024, 12(12), 2215; https://doi.org/10.3390/jmse12122215 - 2 Dec 2024
Cited by 4 | Viewed by 1329
Abstract
The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, [...] Read more.
The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, extended propagation delays, and node mobility. Additionally, the void hole problem in submerged environments arises due to randomized node deployment. To curtail these issues, this paper introduces a novel way of strategically deploying the nodes based on the underwater depth parameters, which can reduce the likelihood of void hole occurrence. An optimal number of clusters based on the fixed transmission range of cluster heads is used to cater to extensive energy usage. In the proposed routing protocol, the path selection is based on the residual energy, link quality, and proximity to a higher number of nodes. Extensive simulations have been conducted by varying network parameters to analyze the network performance in terms of energy expenditure, packet delivery ratio, network throughput, number of dead nodes, and end-to-end delays. Also, the proposed work provides a performance comparison with some state-of-the-art protocols and exhibits promising results. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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14 pages, 1401 KB  
Article
An Offline Reinforcement Learning Approach for Path Following of an Unmanned Surface Vehicle
by Zexing Zhou, Tao Bao, Jun Ding, Yihong Chen, Zhengyi Jiang and Bo Zhang
J. Mar. Sci. Eng. 2024, 12(12), 2173; https://doi.org/10.3390/jmse12122173 - 28 Nov 2024
Cited by 5 | Viewed by 2180
Abstract
Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering [...] Read more.
Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering control policy using a pre-collected dataset of USV path-following trials. A Markov decision process (MDP) tailored for path following is formulated. The proposed offline RL steering controller, trained on static datasets, demonstrates improved sample efficiency and asymptotic performance due to an expanded ensemble of Q-networks. The accuracy and adaptive learning capabilities of the RL controller are validated through simulations and free-running tests. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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Review

Jump to: Research

28 pages, 3059 KB  
Review
From Machinery to Biology: A Review on Mapless Autonomous Underwater Navigation
by Wenxi Zhu and Weicheng Cui
J. Mar. Sci. Eng. 2025, 13(11), 2202; https://doi.org/10.3390/jmse13112202 - 19 Nov 2025
Cited by 1 | Viewed by 1155
Abstract
Autonomous navigation in unknown; map-free environments is a core requirement for advanced robotics. While significant breakthroughs have been achieved in terrestrial scenarios, extending this capability to the unstructured, dynamic, and harsh underwater domain remains an enormous challenge. This review comprehensively analyzes the mainstream [...] Read more.
Autonomous navigation in unknown; map-free environments is a core requirement for advanced robotics. While significant breakthroughs have been achieved in terrestrial scenarios, extending this capability to the unstructured, dynamic, and harsh underwater domain remains an enormous challenge. This review comprehensively analyzes the mainstream technologies underpinning mapless autonomous underwater navigation, with a primary focus on conventional Autonomous Underwater Vehicles (AUVs). It systematically examines key technical pillars of AUV navigation, including Dead Reckoning and Simultaneous Localization and Mapping (SLAM). Furthermore, inspired by the emerging concept of fourth-generation submersibles—which leverage living organisms rather than conventional machinery—this review expands its scope to include live fish as potential controlled platforms for underwater navigation. It first dissects the sophisticated sensory systems and hierarchical navigational strategies that enable aquatic animals to thrive in complex underwater habitats. Subsequently, it categorizes and evaluates state-of-the-art methods for controlling live fish via Brain-Computer Interfaces (BCIs), proposing a three-stage control hierarchy: Direct Motor Control, Semi-Autonomous Control with Task-Level Commands, and Autonomous Control by Biological Intelligence. Finally, the review summarizes current limitations in both conventional AUV technologies and bio-hybrid systems and outlines future directions, such as integrating external sensors with fish, developing onboard AI for adaptive control, and constructing bio-hybrid swarms. This work bridges the gap between robotic engineering and biological inspiration, providing a holistic reference for advancing mapless autonomous underwater navigation. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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