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Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition

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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 (10 February 2026) | Viewed by 19629

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Prof. Dr. Yunsheng Fan

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Guest Editor

College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: unmanned ships; intelligent navigation; autonomous control
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yan Yan

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Guest Editor

College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: guidance; navigation and control of marine vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Dongdong Mu

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Guest Editor

College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: unmanned surface vehicle; intelligent decision; nonlinear control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Marine Science and Engineering is pleased to announce a Special Issue entitled “Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition”, which is based on the great success of our previous Special Issue with the same title.

Marine transportation and traffic are pivotal aspects of global connectivity, drawing significant global attention within the research domain. Within this field, unmanned marine vehicles emerge as crucial assets, including ships, boats, underwater vehicles, underwater gliders, etc. These vehicles are often empowered by advancements in navigation, control, and sensing technologies, and have the potential to redefine the possibilities of marine exploration and operations. This Special Issue serves as a platform for the latest advancements in this dynamic field.

Authors are invited to contribute research focusing on perception technologies, planning algorithms, control strategies, and sensing approaches tailored for unmanned marine vehicles. This collection aims to showcase interdisciplinary efforts in order to drive innovation in autonomous marine systems. Topics of interest include, but are not limited to, the environmental perception of marine vehicles, the autonomous navigation and control of marine vehicles, multi-agent technologies, and efficiency assessment and testing.

Moreover, this Special Issue will play a crucial role in intelligent maritime navigation, which is undoubtedly one of the most focal topics in marine research. This defines the future direction of marine research and promotes the development of various industries and fields.

It aims to explore the advancements, challenges, and applications of unmanned autonomous maritime vehicles, as well to provide insights into their role in enhancing efficiency, safety, intelligence level, and sustainability in maritime operations.

The development of unmanned autonomous maritime vehicles traces back to the late 20th century, with early prototypes primarily being remote-controlled. Over time, advancements in technology, particularly in artificial intelligence, sensor systems, and communication networks, have propelled these vehicles into sophisticated autonomous systems capable of independently executing complex missions.

Cutting-edge research in this field includes enhancing the autonomy and intelligence of maritime vehicles through the use of machine learning and neural networks, thus improving their navigational capabilities in challenging environments and integrating novel sensor technologies for more precise data collection and analysis.

We are seeking original research papers that delve into the design, development, applications, and future prospects of unmanned autonomous maritime vehicles. Papers should offer valuable insights into technological innovations, and application challenges whilst emphasizing the broader impact of these vehicles on various areas relating to perception, planning, and control and swarm.

Prof. Dr. Yunsheng Fan
Prof. Dr. Yan Yan
Dr. Dongdong Mu
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

intelligent multi-agent systems
intelligent control of ships and ocean vehicles
intelligent control of underwater vehicles
perception and sensor fusion for marine vehicles
unmanned system game confrontation
image processing for ocean navigation
AI-based path planning and collision avoidance
hydrodynamic modeling and fluid dynamics
control of unmanned marine vehicles in special scenarios
field verification of unmanned marine vehicles
development trend of unmanned systems in the future
emerging technologies in maritime autonomy

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Unmanned Marine Vehicles: Perception, Planning, Control and Swarm in Journal of Marine Science and Engineering (21 articles)

Published Papers (8 papers)

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Research

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27 pages, 5050 KB

Open AccessArticle

A High-Density Bathymetric Data Model and System Construction Approach Integrated with S-100 for Unmanned Surface Vessel Intelligent Navigation

by Jianan Luo, Zhichen Liu, Haifeng Tang, Chenchen Jiao, Xiongfei Geng and Hua Guo

J. Mar. Sci. Eng. 2026, 14(7), 633; https://doi.org/10.3390/jmse14070633 - 30 Mar 2026

Viewed by 382

Abstract

Intelligent vessel navigation increasingly demands high-density bathymetric data. To resolve the limitations of traditional standards and overcome existing management bottlenecks, this study proposes a novel methodology for high-density bathymetric data modeling and system construction integrated with the S-100 framework. Centered on the International Hydrographic Organization (IHO) S-102 standard, this methodology pioneers a strongly correlated management paradigm for datasets, data, and metadata. Leveraging a relational database architecture and a three-level indexing mechanism, it enables the structured organization and efficient retrieval of data throughout its entire life cycle. At the data production stage, geometric feature constraints based on convex hulls are innovatively incorporated to facilitate the interpolation of high-density water depth data and the generation of grid arrays. A data organization and structured storage model based on the three-tier logical architecture of the Hierarchical Data Format version 5 (HDF5) is proposed, which couples the technologies of block-based storage and refined version control to achieve the synergistic optimization of storage costs and access efficiency for high-density water depth data. Validation via field measurements in selected sea areas of the East China Sea demonstrated that the generated S-102 bathymetric data complied with international specifications and achieved excellent terrain restoration accuracy. Meanwhile, the proposed HDF5-based storage strategy achieves a storage space reduction of 83.6%. This research provides authoritative and efficient data support for scenarios such as intelligent navigation and port digitalization, and contributes to the construction of an intelligent shipping ecosystem. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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16 pages, 4129 KB

Open AccessArticle

A Distributed Maritime Target Classification Method Based on Broad Learning and MobilityFirst

by Zhenqi Wang, Fei Teng, Shilong Liu, Liang-En Yuan and Rui Wang

J. Mar. Sci. Eng. 2026, 14(5), 499; https://doi.org/10.3390/jmse14050499 - 6 Mar 2026

Viewed by 288

Abstract

Marine target classification is a key technology for unmanned surface vehicles (USVs) to perform ocean surveillance. Traditional maritime target classification methods require improvements in both accuracy and processing speed when handling classification tasks. In this paper, a distributed maritime target classification (DMTC) method based on broad learning and MobilityFirst is proposed. Firstly, a multi-model collaborative classification and fusion framework is proposed to achieve feature consistency fusion. Secondly, to enhance the security and privacy of communication in autonomous surface vehicles, the MobilityFirst approach is employed to improve information complementarity among multiple models within the distributed framework. Finally, the broad learning system, as the model’s classification layer, reduces the training complexity. Extensive experimental results demonstrate that this proposed approach surpasses single-model and distributed methods in accuracy, F1 score, and the area under the precision–recall curve (AUPR). This approach offers a clear advantage in multi-ship classification tasks while simultaneously enhancing the model’s generalization capability. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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31 pages, 2539 KB

Open AccessArticle

Leader–Follower Motion Control System for a Group of AUVs via Hybrid Measurement Sparse LBL Navigation

by Aleksey Kabanov, Kirill Dementiev and Vadim Kramar

J. Mar. Sci. Eng. 2026, 14(4), 358; https://doi.org/10.3390/jmse14040358 - 12 Feb 2026

Viewed by 472

Abstract

Autonomous navigation of underwater vehicles in infrastructure-limited environments presents persistent challenges due to the constraints of traditional acoustic positioning systems. Sparse long baseline (sparse LBL) navigation, which relies on a minimal set of acoustic transponders, offers a promising alternative but suffers from geometric ambiguity and reduced robustness without external aiding. This paper introduces an integrated approach to measurement-based navigation and control in the sparse LBL setting with two base transponders, focusing on three key components. First, a novel three-stage navigation algorithm is proposed, which enables unambiguous robust leader–follower formation position estimation using only two acoustic transponders and onboard measurements. Second, a hybrid state estimation framework is developed to fuse asynchronous data from inertial sensors, depth measurements, and acoustic ranging, accommodating measurement uncertainty and timing variability. Third, there is a nonlinear trajectory tracking controller based on state-dependent coefficients (SDCs) technique. The combined approach enables accurate and robust leader–follower structure navigation with minimal acoustic infrastructure and is suitable for deployment in dynamic or remote underwater scenarios. The numerical simulations demonstrate the acceptable motion control accuracy. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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28 pages, 4527 KB

Open AccessArticle

Enhanced Adaptive QPSO-Enabled Game-Theoretic Model Predictive Control for AUV Pursuit–Evasion Under Velocity Constraints

by Duan Gao, Mingzhi Chen and Yunhao Zhang

J. Mar. Sci. Eng. 2026, 14(3), 318; https://doi.org/10.3390/jmse14030318 - 6 Feb 2026

Viewed by 454

Abstract

Pursuit–evasion involves coupled, antagonistic decision-making and is prone to local-optimal behaviors when solved online under nonlinear dynamics and constraints. This study investigates a dual-AUV pursuit–evasion problem in ocean-current environments by integrating game theory with model predictive control (MPC). We formulated a game-theoretic MPC scheme that optimizes pursuit and evasion actions over a finite receding horizon, producing Nash-like responses. To solve the resulting nonconvex and multi-modal optimization problems reliably, we developed an Enhanced Adaptive Quantum Particle Swarm Optimization (EA-QPSO) method that incorporates chaos-based initialization and adaptive diversity-aware exploration with stagnation-escape perturbations. EA-QPSO is benchmarked against representative solvers, including fmincon, Differential Evolution (DE), and the Marine Predator Algorithm (MPA). Extensive 2D and 3D simulations demonstrate that EA-QPSO mitigates local-optimum trapping and yields more effective closed-loop behaviors, achieving longer escaping trajectories and more persistent pursuit until capture under the game formulation. In 3D scenarios, EA-QPSO better preserves high-speed motion while coordinating agile angular-rate adjustments, outperforming competing methods that exhibit premature deceleration or degraded maneuvering. These results validate the proposed framework for computing reliable competitive strategies in constrained underwater pursuit–evasion games. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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36 pages, 5570 KB

Open AccessArticle

Evolving Collective Intelligence for Unmanned Marine Vehicle Swarms: A Federated Meta-Learning Framework for Cross-Fleet Planning and Control

by Yuhan Ye, Hongjun Tian, Yijie Yin, Yuhan Zhou, Yang Xiong, Zi Wang, Yaojiang Liu, Zinan Nie, Zitong Zhang, Yichen Wang and Jingyu Sun

J. Mar. Sci. Eng. 2026, 14(1), 82; https://doi.org/10.3390/jmse14010082 - 31 Dec 2025

Viewed by 597

Abstract

The development of robust autonomous maritime systems is fundamentally constrained by the “data silo” problem, where valuable operational data from disparate fleets remain isolated due to privacy concerns, severely limiting the scalability of general-purpose navigation intelligence. To address this barrier, we propose a novel Federated Meta-Transfer Learning (FMTL) framework that enables collaborative evolution of unmanned surface vehicle (USV) swarms while preserving data privacy. Our hierarchical approach orchestrates three synergistic stages: (1) transfer learning pre-trains a universal “Sea-Sense” foundation model on large-scale maritime data to establish fundamental navigation priors; (2) federated learning enables decentralized fleets to collaboratively refine this model through encrypted gradient aggregation, forming a distributed cognitive network; (3) meta-learning allows for rapid personalization to individual vessel dynamics with minimal adaptation trials. Comprehensive simulations across heterogeneous fleet distributions demonstrate that our federated model achieves a 95.4% average success rate across diverse maritime scenarios, significantly outperforming isolated specialist models (63.9–73.1%), while enabling zero-shot performance of 78.5% and few-shot adaptation within 8–12 episodes on unseen tasks. This work establishes a scalable, privacy-preserving paradigm for collective maritime intelligence through swarm-based learning. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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17 pages, 4478 KB

Open AccessArticle

A Study on Generating Maritime Image Captions Based on Transformer Dual Information Flow

by Zhenqiang Zhao, Helong Shen, Meng Wang and Yufei Wang

J. Mar. Sci. Eng. 2025, 13(7), 1204; https://doi.org/10.3390/jmse13071204 - 21 Jun 2025

Cited by 1 | Viewed by 1238

Abstract

The environmental perception capability of intelligent ships is essential for enhancing maritime navigation safety and advancing shipping intelligence. Image caption generation technology plays a pivotal role in this context by converting visual information into structured semantic descriptions. However, existing general purpose models often struggle to perform effectively in complex maritime environments due to limitations in visual feature extraction and semantic modeling. To address these challenges, this study proposes a transformer dual-stream information (TDSI) model. The proposed model uses a Swin-transformer to extract grid features and combines them with fine-grained scene semantics obtained via SegFormer. A dual-encoder structure independently encodes the grid and segmentation features, which are subsequently fused through a feature fusion module for implicit integration. A decoder with a cross-attention mechanism is then employed to generate descriptive captions for maritime images. Extensive experiments were conducted using the constructed maritime semantic segmentation and maritime image captioning datasets. The results demonstrate that the proposed TDSI model outperforms existing mainstream methods in terms of several evaluation metrics, including BLEU, METEOR, ROUGE, and CIDEr. These findings confirm the effectiveness of the TDSI model in enhancing image captioning performance in maritime environments. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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30 pages, 9151 KB

Open AccessArticle

Research on LSTM-PPO Obstacle Avoidance Algorithm and Training Environment for Unmanned Surface Vehicles

by Wangbin Luo, Xiang Wang, Fang Han, Zhiguo Zhou, Junyu Cai, Lin Zeng, Hong Chen, Jiawei Chen and Xuehua Zhou

J. Mar. Sci. Eng. 2025, 13(3), 479; https://doi.org/10.3390/jmse13030479 - 28 Feb 2025

Cited by 10 | Viewed by 4746

Abstract

The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface environment, the training set is triggered. The reward function does not match the actual situation, resulting in a poor obstacle avoidance effect. In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. The engine builds a USV physical model and a refined water deep reinforcement learning training environment including a variety of obstacle models. The experimental results demonstrate that the LSTM-PPO algorithm exhibits an effective and rational obstacle avoidance effect, with a success rate of 86.7%, an average path length of 198.52 m, and a convergence time of 1.5 h. A comparison with the performance of three other deep reinforcement learning algorithms reveals that the LSTM-PPO algorithm exhibits a 21.5% reduction in average convergence time, an 18.5% reduction in average path length, and an approximately 20% enhancement in the success rate of obstacle avoidance in complex environments. These results indicate that the LSTM-PPO algorithm can effectively enhance the search efficiency and optimize the path planning in obstacle avoidance for unmanned boats, rendering it more rational. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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Review

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33 pages, 10200 KB

Open AccessReview

Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research

by Zhichao Lv, Xiangyu Wang, Gang Wang, Xuefei Xing, Chenlong Lv and Fei Yu

J. Mar. Sci. Eng. 2025, 13(5), 969; https://doi.org/10.3390/jmse13050969 - 16 May 2025

Cited by 18 | Viewed by 10144

Abstract

Unmanned Surface Vehicles (USVs) have emerged as vital tools in marine monitoring and management due to their high efficiency, low cost, and flexible deployment capabilities. This paper presents a systematic review focusing on four core areas of USV applications: communication networking, navigation, control, and data-driven operations. First, the characteristics and challenges of acoustic, electromagnetic, and optical communication methods for USV networking are analyzed, with an emphasis on the future trend toward multimodal communication integration. Second, a comprehensive review of global navigation, local navigation, cooperative navigation, and autonomous navigation technologies is provided, highlighting their applications and limitations in complex environments. Third, the evolution of USV control systems is examined, covering group control, distributed control, and adaptive control, with particular attention given to fault tolerance, delay compensation, and energy optimization. Finally, the application of USVs in data-driven marine tasks is summarized, including multi-sensor fusion, real-time perception, and autonomous decision-making mechanisms. This study aims to reveal the interaction and coordination mechanisms among communication, navigation, control, and data-driven operations from a system integration perspective, providing insights and guidance for the intelligent operations and comprehensive applications of USVs in marine environments. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

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