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New Technologies in Autonomous Ship Navigation
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New Technologies in Autonomous Ship Navigation

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 September 2026 | Viewed by 5274

Special Issue Editors

Dr. Jiqiang Li

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: marine vehicles; path following; robust control; guidance; nonlinear control; cooperative control; USV-UAV
Prof. Dr. Guoqing Zhang

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: robust control for multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio M. Pascoal

E-Mail Website
Guest Editor
Dynamical Systems and Ocean Robotics Lab, University of Lisbon, Lisbon, Portugal
Interests: cooperative control of marine vehicles; path following control; adaptive control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weidong Zhang

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: marine vehicles; robust control; intelligent control; guidance; nonlinear control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements have propelled autonomous ship navigation to the forefront of maritime innovation driven by challenging operational demands in global shipping, along with growing requirements for enhanced safety and efficiency. Significant progress in sensor fusion and actuation systems, artificial intelligence, and connectivity now enables vessels to perceive complex environments, make real-time decisions, and execute precise maneuvers with minimal human intervention. This Special Issue aims to integrate cutting-edge developments such as next-generation methods and systems for enhanced perception and situational awareness, AI-driven collision avoidance, adaptive path planning, multi-vessel operations, cross-domain collaboration, and resilient communication architectures. The purpose of this Special Issue in the Journal of Marine Science and Engineering is to present pioneering research and engineering solutions advancing the reliability and intelligence of autonomous maritime operation. Topics of interest include, but are not limited to, the following: marine surface vehicles, sensor-fusion navigation technology, autonomous ship navigation, optimal path planning, dynamic positioning, and intelligent control for multiple vehicles.

Dr. Jiqiang Li
Prof. Dr. Guoqing Zhang
Dr. Antonio M. Pascoal
Prof. Dr. Weidong Zhang
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 surface vehicles
  • sensor-fusion navigation technology
  • autonomous ship navigation
  • optimal path planning
  • dynamic positioning
  • cooperative control
  • intelligent control for multiple vehicles

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Published Papers (7 papers)

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Research

20 pages, 7417 KB  
Article
MAAT: A Marine-Aware Adaptive Tracker for Robust and Real-Time Multi-Object Tracking in Maritime Environments
by Xinjie Han, Qi Han, Yunsheng Fan and Dongdong Mu
J. Mar. Sci. Eng. 2026, 14(8), 738; https://doi.org/10.3390/jmse14080738 - 16 Apr 2026
Abstract
Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquired on maritime platforms typically suffer from challenges such as platform-induced jitter [...] Read more.
Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquired on maritime platforms typically suffer from challenges such as platform-induced jitter and nonlinear object motion, which significantly degrade tracking performance. To address these challenges, this paper builds upon ByteTrack by incorporating an adaptive Kalman filtering scheme and proposing a density-aware association strategy, resulting in a novel tracker termed the Marine-Aware Adaptive Tracker (MAAT). Specifically, an adaptive Kalman filter is introduced to increase the contribution of high-confidence detections during the state update process, thereby enhancing the stability and robustness of state estimation. Furthermore, to better mitigate the frequent identity switches caused by severe platform jitter from the USV observation platform, a density-aware association strategy is proposed. This strategy dynamically adjusts the composition of the cost matrix according to the density of high-confidence targets, enabling more reliable data association under varying scene conditions. Finally, the proposed tracking algorithm is evaluated against several state-of-the-art methods on the Singapore Maritime Dataset. It achieves competitive performance, attaining 44.37 MOTA and 43.857 IDF1. Moreover, MAAT operates in real time, running at 41.4 FPS. The experimental results demonstrate that MAAT is capable of performing accurate and real-time multi-object tracking in dynamic maritime environments with surface fluctuations, thereby providing effective technical support for intelligent maritime surveillance applications. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
20 pages, 1284 KB  
Article
Practical L1-Based Guidance and Neural Path-Following Control for Underactuated Ships with Backlash Hysteresis
by Chenfeng Huang, Bingyan Zhang, Haitong Xu and Meirong Wei
J. Mar. Sci. Eng. 2026, 14(4), 402; https://doi.org/10.3390/jmse14040402 - 22 Feb 2026
Viewed by 327
Abstract
The study addresses trajectory tracking control for underactuated vessels with uncertain backlash-type hysteresis. First, an improved practical L1-based guidance strategy is developed by embedding the L1 mechanism into the virtual ship framework to eliminate steering overshoot and yaw angle error accumulation, which can [...] Read more.
The study addresses trajectory tracking control for underactuated vessels with uncertain backlash-type hysteresis. First, an improved practical L1-based guidance strategy is developed by embedding the L1 mechanism into the virtual ship framework to eliminate steering overshoot and yaw angle error accumulation, which can facilitate the smooth turning of ships along waypoint-based paths with large curvature. Next, to mitigate control performance degradation induced by backlash-like hysteresis nonlinearity, an improved quadratic function is utilized to boost the closed-loop system’s convergence capability. Moreover, system model uncertainty-induced perturbations are compensated using the resilient neural damping method, which can simplify the structure and reduce the computation burden of the proposed controller. Utilizing Lyapunov-based approaches and the special Young’s inequality, uniformly ultimately bounded stability over a semi-global domain is established. Finally, numerical simulations are executed to validate the efficacy of the developed control architecture. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
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24 pages, 2582 KB  
Article
A Novel Approach for Vessel Graphics Identification and Augmentation Based on Unsupervised Illumination Estimation Network
by Jianan Luo, Zhichen Liu, Chenchen Jiao and Mingyuan Jiang
J. Mar. Sci. Eng. 2025, 13(11), 2167; https://doi.org/10.3390/jmse13112167 - 17 Nov 2025
Viewed by 567
Abstract
Vessel identification in low-light environments is a challenging task since low-light images contain less information for detecting objects. To improve the feasibility of vessel identification in low-light environments, we present a new unsupervised low-light image augmentation approach to augment the visibility of vessel [...] Read more.
Vessel identification in low-light environments is a challenging task since low-light images contain less information for detecting objects. To improve the feasibility of vessel identification in low-light environments, we present a new unsupervised low-light image augmentation approach to augment the visibility of vessel features in low-light images, laying a foundation for subsequent identification. This guarantees the feasibility of vessel identification with the augmented image. To this end, we design an illumination estimation network (IEN) to estimate the illumination of a low-light image based on the Retinex theory. Then, we augment the low-light image by estimating its reflectance with the estimated illumination. Compared with the existing deep learning-based supervised low-light image augmentation approach that depends on the low- and normal-light image pairs for model training, IEN is an unsupervised approach without using normal-light image as references during model training. Compared with the traditional unsupervised low-light image augmentation approach, IEN shows faster image augmentation speed by parallel computation acceleration with image Processing Units (GPUs). The proposed approach builds an end-to-end pipeline integrating a vessel-aware weight matrix and SmoothNet, which optimizes illumination estimation under the Retinex framework. To evaluate the effectiveness of the proposed approach, we build a low-light vessel image set based on the Sea Vessels 7000 dataset—a public maritime image set containing 7000 vessel images across multiple categories Then, we carry out an experiment to evaluate the feasibility of vessel identification using the augmented image. Experimental results show that the proposed approach boosts the AP75 metric of the RetinaNet detector by 6.6 percentage points (from 56.8 to 63.4) on the low-light Sea Vessels 7000 dataset, confirming that the augmented image significantly improves vessel identification accuracy in low-light scenarios. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
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19 pages, 8707 KB  
Article
Event-Triggered Optimal Path-Following Control for Wind-Assisted Autonomous Surface Vehicles via Actor–Critic Reinforcement Learning
by Zhihao Li, Guoqing Zhang and Peng Liu
J. Mar. Sci. Eng. 2025, 13(11), 2117; https://doi.org/10.3390/jmse13112117 - 8 Nov 2025
Cited by 2 | Viewed by 764
Abstract
This paper proposes an enhanced event-triggered optimal control scheme integrated with reinforcement learning (RL) for wind-assisted autonomous surface vehicles (WAASVs), aiming to ensure the safety and energy efficiency of marine path-following missions. To address the uncertainties arising from the dynamic model and time-varying [...] Read more.
This paper proposes an enhanced event-triggered optimal control scheme integrated with reinforcement learning (RL) for wind-assisted autonomous surface vehicles (WAASVs), aiming to ensure the safety and energy efficiency of marine path-following missions. To address the uncertainties arising from the dynamic model and time-varying external disturbances, a reinforcement learning approach based on the architecture of actor–critic neural networks (AC-NNs) is employed to generate control signals without relying on precise model knowledge while minimizing path deviation and energy consumption. Furthermore, an integral event-triggered control (IETC) algorithm is developed to dynamically adjust the control signal updates according to the system output errors, which offers a promising solution to prevent excessive mechanical wear of the actuators. The stability of all error variables is rigorously analyzed using the Lyapunov theory. Finally, two simulation experiments on the rotor-assisted vehicle are performed to validate the superior tracking performance and practical applicability of the proposed algorithm. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
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29 pages, 4097 KB  
Article
Global Fixed-Time Target Enclosing Tracking Control for an Unmanned Surface Vehicle Under Unknown Velocity States and Actuator Saturation
by Xinjie Han, Guanglu Ma, Yunsheng Fan, Dongdong Mu, Feng Sun, Linlong Shi and Hongbiao Li
J. Mar. Sci. Eng. 2025, 13(11), 2094; https://doi.org/10.3390/jmse13112094 - 3 Nov 2025
Viewed by 782
Abstract
This paper presents a global fixed-time control framework to address the target circumnavigation tracking problem of underactuated unmanned surface vehicles (USV) under unknown velocity states, lumped uncertainties, and actuator saturation. At the core of this approach is a novel fixed-time target-enclosing line-of-sight (FTTELOS) [...] Read more.
This paper presents a global fixed-time control framework to address the target circumnavigation tracking problem of underactuated unmanned surface vehicles (USV) under unknown velocity states, lumped uncertainties, and actuator saturation. At the core of this approach is a novel fixed-time target-enclosing line-of-sight (FTTELOS) guidance law, designed to generate the desired heading angle and surge velocity. To estimate unknown velocities, external disturbances, and unmeasured system states, a set of fixed-time observers is constructed, consisting of a velocity observer, a disturbance observer, and a high-dimensional extended state observer (HFTESO). Moreover, to enhance robustness and effectively tackle actuator saturation, the control scheme incorporates a fixed-time sliding mode controller, a dynamic auxiliary system, and a fixed-threshold event-triggered mechanism. Simulation results using SimuNPS demonstrate that the proposed method enables rapid and smooth target circumnavigation, with all system errors converging to an arbitrarily small neighborhood of the origin within a fixed time. Theoretical analysis and simulation studies confirm the effectiveness and robustness of both the FTTELOS guidance law and the integrated control strategy. Quantitatively, compared with the traditional target-enclosing line-of-sight (TELOS) method, the proposed FTTELOS reduces the convergence time of the distance error δe from 13.64 s to 10.22 s and the angular error ϕe from 10.46 s to 7.52 s, demonstrating a significant improvement in convergence speed and overall control performance. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
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24 pages, 1908 KB  
Article
Integrated LiDAR-Based Localization and Navigable Region Detection for Autonomous Berthing of Unmanned Surface Vessels
by Haichao Wang, Yong Yin, Liangxiong Dong and Helang Lai
J. Mar. Sci. Eng. 2025, 13(11), 2079; https://doi.org/10.3390/jmse13112079 - 31 Oct 2025
Cited by 2 | Viewed by 1179
Abstract
Autonomous berthing of unmanned surface vehicles (USVs) requires high-precision positioning and accurate detection of navigable region in complex port environments. This paper presents an integrated LiDAR-based approach to address these challenges. A high-precision 3D point cloud map of the berth is first constructed [...] Read more.
Autonomous berthing of unmanned surface vehicles (USVs) requires high-precision positioning and accurate detection of navigable region in complex port environments. This paper presents an integrated LiDAR-based approach to address these challenges. A high-precision 3D point cloud map of the berth is first constructed by fusing LiDAR data with real-time kinematic (RTK) measurements. USV pose is then estimated by matching real-time LiDAR scans to the prior map, achieving robust, RTK-independent localization. For safe navigation, a novel navigable region detection algorithm is proposed, which combines point cloud projection, inner-boundary extraction, and target clustering. This method accurately identifies quay walls and obstacles, generating reliable navigable areas and ensuring collision-free berthing. Field experiments conducted in Ling Shui Port, Dalian, China, validate the proposed approach. Results show that the map-based positioning reduces absolute trajectory error (ATE) by 55.29% and relative trajectory error (RTE) by 38.71% compared to scan matching, while the navigable region detection algorithm provides precise and stable navigable regions. These outcomes demonstrate the effectiveness and practical applicability of the proposed method for autonomous USV berthing. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
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25 pages, 2727 KB  
Article
Berthing State Estimation for Autonomous Surface Vessels Using Ship-Based 3D LiDAR
by Haichao Wang, Yong Yin, Qianfeng Jing and Chen-Liang Zhang
J. Mar. Sci. Eng. 2025, 13(10), 1975; https://doi.org/10.3390/jmse13101975 - 15 Oct 2025
Cited by 1 | Viewed by 965
Abstract
Automated berthing remains a critical challenge for autonomous surface vessels (ASVs), necessitating precise berthing state estimation as a fundamental prerequisite. In this paper, we present a novel berthing state estimation method tailored for ASVs and based on 3D LiDAR technology. Firstly, a berthing [...] Read more.
Automated berthing remains a critical challenge for autonomous surface vessels (ASVs), necessitating precise berthing state estimation as a fundamental prerequisite. In this paper, we present a novel berthing state estimation method tailored for ASVs and based on 3D LiDAR technology. Firstly, a berthing plane acquisition scheme based on point cloud plane fitting is proposed; the feasibility of the scheme was verified by experiments. The point cloud registration algorithm was used to realize the ship pose estimation. Before registration, the preprocessing technology was used to filter out the noise and outliers in the point cloud data to improve the accuracy of pose estimation. A detailed method for calculating the berthing state information is proposed. This method considers the influence of ship roll, pitch, and yaw during berthing, and ensures the accuracy of the obtained state information. Finally, a real-time ship berthing perception framework was constructed using the Robot Operating System (ROS), enabling the continuous output of vital berthing state information, including berthing distance, velocity, approaching angle, and yaw rate, at a frequency of 10 Hz. To validate the effectiveness of our algorithm, extensive real ship experiments were conducted, yielding highly promising results. The average angle error was found to be less than 0.26°, with an average distance error below 0.023 m. Full article
(This article belongs to the Special Issue New Technologies in Autonomous Ship Navigation)
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