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Unmanned Marine Vehicles: Perception, Planning, Control and Swarm

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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: 10 December 2024 | Viewed by 7635

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Special Issue Editors

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

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

Dr. Dongdong Mu

E-Mail Website
Guest Editor

College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: unmanned surface vehicle; intelligent decision; nonlinear control
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Special Issue Information

Dear Colleagues,

Marine transportation and traffic are pivotal aspects of global connectivity, drawing significant global scholarly attention. Within this domain, unmanned marine vehicles emerge as crucial assets; this includes 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, titled Unmanned Marine Vehicles: Perception, Planning, Control and Swarm, 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.

This Special Issue, titled Unmanned Marine Vehicles: Perception, Planning, Control and Swarm, has played 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.

This Special Issue 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 which are 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, application challenges, as well as 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 100 words) can be sent to the Editorial Office for announcement on this website.

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

intelligent agents and multi-agent systems
intelligent control of ships and ocean vehicles
intelligent control of underwater vehicles
decision inference problem for multi-agent systems
intelligent perception of unmanned systems
unmanned system game confrontation
image processing for ocean navigation
path planning and collision avoidance technology
fluid dynamics calculation of unmanned aerial vehicles
control of unmanned marine vehicles in special scenarios
field verification of unmanned systems
development trend of unmanned systems in the future

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Published Papers (12 papers)

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Research

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18 pages, 31105 KiB

Open AccessArticle

Global Path Planning of Unmanned Surface Vehicle in Complex Sea Areas Based on Improved Streamline Method

by Haoran Liu, Qihe Shan, Yuchi Cao and Qi Xu

J. Mar. Sci. Eng. 2024, 12(8), 1324; https://doi.org/10.3390/jmse12081324 - 5 Aug 2024

Viewed by 399

Abstract

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios. Full article

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

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18 pages, 1954 KiB

Open AccessArticle

Real-Time Underwater Fish Detection and Recognition Based on CBAM-YOLO Network with Lightweight Design

by Zheping Yan, Lichao Hao, Jianmin Yang and Jiajia Zhou

J. Mar. Sci. Eng. 2024, 12(8), 1302; https://doi.org/10.3390/jmse12081302 - 1 Aug 2024

Viewed by 414

Abstract

More and more underwater robots are deployed to investigate marine biodiversity autonomously, and tools are needed by underwater robots to discover and acknowledge marine life. This paper has proposed a convolutional neural network-based method for intelligent fish detection and recognition with a dataset used for training and testing generated and augmented from an open-source Fish Database regarding 6 different types. Firstly, to improve image quality, a hybrid image enhancement algorithm is used to preprocess underwater images with a weighted fusion strategy of multiple traditional methodologies and comparisons have been made to prove the effectiveness according to various indexes. Secondly, to increase detection and recognition accuracy, different attention modules are integrated into the YOLOv5m network structure and the convolutional block attention module(CBAM) has outperformed other modules in recall rate and mAP while maintaining the capability of real-time processing. Lastly, to meet real-time requirements, lightweight adjustments have been made to CBAM-YOLOv5m with the GSConv module and C3Ghost module and a nearly 25% reduction in network parameters and a 20% reduction in computational consumption are obtained. Besides, the lightweight network has realized better accuracy than YOLOv5m. In conclusion, the method proposed in this paper is effective in real-time fish detection and recognition with practical application prospects. Full article

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

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13 pages, 8341 KiB

Open AccessArticle

Multi-Autonomous Underwater Vehicle Full-Coverage Path-Planning Algorithm Based on Intuitive Fuzzy Decision-Making

by Xiaomeng Zhang, Xuewei Hao, Lichuan Zhang, Lu Liu, Shuo Zhang and Ranzhen Ren

J. Mar. Sci. Eng. 2024, 12(8), 1276; https://doi.org/10.3390/jmse12081276 - 29 Jul 2024

Viewed by 395

Abstract

Aiming at the difficulty of realizing full-coverage path planning in a multi-AUV collaborative search, a multi-AUV full-coverage path-planning algorithm based on intuitionistic fuzzy decision-making is proposed. First, the state space model of the search environment was constructed using the raster method to provide accurate environment change data for the AUV. Second, the full-coverage path-planning algorithm for the multi-AUV collaborative search was constructed using intuition-based fuzzy decision-making, and more uncertain underwater information was modeled using the intuition-based fuzzy decision algorithm. A priority strategy was used to avoid obstacles in the search area. Finally, the simulation experiment verified the proposed algorithm. The results demonstrate that the proposed algorithm can effectively realize full-coverage path planning of the search area, and the priority strategy can effectively reduce the generation of repeated paths. Full article

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

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17 pages, 1317 KiB

Open AccessArticle

Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles

by Jing Li, Lili Wan, Zhen Huang, Yan Chen and Huiying Tang

J. Mar. Sci. Eng. 2024, 12(8), 1268; https://doi.org/10.3390/jmse12081268 - 28 Jul 2024

Viewed by 439

Abstract

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS. Full article

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

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24 pages, 6600 KiB

Open AccessArticle

Ship Autonomous Berthing Strategy Based on Improved Linear-Quadratic Regulator

by Jian Yin, Guoquan Chen, Shenhua Yang, Zeyang Huang and Yongfeng Suo

J. Mar. Sci. Eng. 2024, 12(8), 1245; https://doi.org/10.3390/jmse12081245 - 23 Jul 2024

Viewed by 376

Abstract

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios. Full article

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

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30 pages, 6135 KiB

Open AccessArticle

A Method for Multi-AUV Cooperative Area Search in Unknown Environment Based on Reinforcement Learning

by Yueming Li, Mingquan Ma, Jian Cao, Guobin Luo, Depeng Wang and Weiqiang Chen

J. Mar. Sci. Eng. 2024, 12(7), 1194; https://doi.org/10.3390/jmse12071194 - 16 Jul 2024

Viewed by 471

Abstract

As an emerging direction of multi-agent collaborative control technology, multiple autonomous underwater vehicle (multi-AUV) cooperative area search technology has played an important role in civilian fields such as marine resource exploration and development, marine rescue, and marine scientific expeditions, as well as in military fields such as mine countermeasures and military underwater reconnaissance. At present, as we continue to explore the ocean, the environment in which AUVs perform search tasks is mostly unknown, with many uncertainties such as obstacles, which places high demands on the autonomous decision-making capabilities of AUVs. Moreover, considering the limited detection capability of a single AUV in underwater environments, while the area searched by the AUV is constantly expanding, a single AUV cannot obtain global state information in real time and can only make behavioral decisions based on local observation information, which adversely affects the coordination between AUVs and the search efficiency of multi-AUV systems. Therefore, in order to face increasingly challenging search tasks, we adopt multi-agent reinforcement learning (MARL) to study the problem of multi-AUV cooperative area search from the perspective of improving autonomous decision-making capabilities and collaboration between AUVs. First, we modeled the search task as a decentralized partial observation Markov decision process (Dec-POMDP) and established a search information map. Each AUV updates the information map based on sonar detection information and information fusion between AUVs, and makes real-time decisions based on this to better address the problem of insufficient observation information caused by the weak perception ability of AUVs in underwater environments. Secondly, we established a multi-AUV cooperative area search system (MACASS), which employs a search strategy based on multi-agent reinforcement learning. The system combines various AUVs into a unified entity using a distributed control approach. During the execution of search tasks, each AUV can make action decisions based on sonar detection information and information exchange among AUVs in the system, utilizing the MARL-based search strategy. As a result, AUVs possess enhanced autonomy in decision-making, enabling them to better handle challenges such as limited detection capabilities and insufficient observational information. Full article

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

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22 pages, 4648 KiB

Open AccessArticle

Obstacle Avoidance Control for Autonomous Surface Vehicles Using Elliptical Obstacle Model Based on Barrier Lyapunov Function and Model Predictive Control

by Pengfei Zhang, Yuanpei Ding and Shuxin Du

J. Mar. Sci. Eng. 2024, 12(6), 1035; https://doi.org/10.3390/jmse12061035 - 20 Jun 2024

Viewed by 511

Abstract

This study explores positioning and obstacle avoidance control for autonomous surface vehicles (ASVs) by considering equivalent elliptical-shaped obstacles. Firstly, compared to most Barrier Lyapunov function (BLF) methods that approximate obstacles as circles, a novel BLF is improved by introducing an elliptical obstacle model. This improvement uses ellipses instead of traditional circles to equivalent obstacles, effectively resolving the issue of excessive conservatism caused by over-expanded areas during the obstacle equivalence process. Secondly, unlike traditional obstacle avoidance approaches based on BLF, to achieve constraint control of angle and angular velocity, a method based on model predictive control (MPC) is introduced to optimize local angle planning. By incorporating angular error constraints, this ensures that the directional error of the ASV remains within a restricted range. Furthermore, an auxiliary function of directional error is introduced into the ASV’s linear velocity, ensuring that the ASV parks and adjusts its direction when the deviation in angle becomes too large. This innovation guarantees the linearization of the ASV system, addressing the complexity of traditional MPC methods when dealing with nonlinear second-order ASV systems. Ultimately, the efficacy of our proposed approach is validated through rigorous experimental simulations conducted on the MATLAB platform. Full article

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

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26 pages, 14766 KiB

Open AccessArticle

Complete Coverage Path Planning Based on Improved Genetic Algorithm for Unmanned Surface Vehicle

by Gongxing Wu, Mian Wang and Liepan Guo

J. Mar. Sci. Eng. 2024, 12(6), 1025; https://doi.org/10.3390/jmse12061025 - 19 Jun 2024

Cited by 1 | Viewed by 630

Abstract

Complete Coverage Path Planning (CCPP) is a key technology for Unmanned Surface Vehicles (USVs) that require complete coverage on the water surface, such as water sample collection, garbage collection, water field patrol, etc. When facing complex and irregular boundaries, the traditional CCPP-based boustrophedon method may encounter many problems and challenges, such as multiple repeated regions, multiple turns, and the easy occurrence of local optima. The traditional genetic algorithm also has some shortcomings. The fixed fitness function, mutation operator and crossover operator are not conducive to the evolution of the population and the production of better offspring. In order to solve the above problems, this paper proposes a CCPP method based on an improved genetic algorithm, including a stretched fitness function, an adaptive mutation operator, and a crossover operator. The algorithm combines the key operators in the fireworks algorithm. Then, the turning and obstacle avoidance during the operation of the Unmanned Surface Vehicle are optimized. Simulation and experiments show that the improved genetic algorithm has higher performance than the exact unit decomposition method and the traditional genetic algorithm, and has more advantages in reducing the coverage path length and repeating the coverage area. This proves that the proposed CCPP method has strong adaptability to the environment and has practical application value in improving the efficiency and quality of USV related operations. Full article

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

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15 pages, 3913 KiB

Open AccessArticle

LWS-YOLOv7: A Lightweight Water-Surface Object-Detection Model

by Zhengzhong Li, Hongxiang Ren, Xiao Yang, Delong Wang and Jian Sun

J. Mar. Sci. Eng. 2024, 12(6), 861; https://doi.org/10.3390/jmse12060861 - 22 May 2024

Viewed by 820

Abstract

In inland waterways, there is a high density of various objects, with a predominance of small objects, which can easily affect navigation safety. To improve the navigation safety of inland ships, this paper proposes a new lightweight water-surface object-detection model named LWS-YOLOv7, which is based on the baseline model YOLOv7. Firstly, the localization loss function is improved and the w-CIoU function is introduced to reduce the model’s sensitivity to position deviations of small objects and to improve the allocation accuracy of positive and negative sample labels. Secondly, a new receptive field amplification module named GSPPCSPC is proposed to reduce the model’s parameters and enhance its receptive field. Thirdly, a small-object feature-fusion layer, P2, is added to improve the recall rate of small objects. Finally, based on the LAMP model pruning method, the weights with lower importance are pruned to simplify the parameters and computational complexity of the model, facilitating the deployment of the model on shipborne devices. The experimental results demonstrate that, compared to the original YOLOv7 model, the map of LWS-YOLOv7 increased by 3.1%, the parameters decreased by 38.8%, and the GFLOPS decreased by 28.8%. Moreover, the model not only has better performance and higher speed for input images of different sizes, but it can also be applied to different meteorological conditions. Full article

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

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21 pages, 1302 KiB

Open AccessArticle

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

by Dan Yu, Hongjian Wang, Xu Cao, Zhao Wang, Jingfei Ren and Kai Zhang

J. Mar. Sci. Eng. 2024, 12(5), 791; https://doi.org/10.3390/jmse12050791 - 8 May 2024

Viewed by 834

Abstract

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios. Full article

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

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18 pages, 3725 KiB

Open AccessArticle

Distributed Formation–Containment Tracking Control for Multi-Hovercraft Systems with Compound Perturbations

by Zhipeng Fan, Yujie Xu and Mingyu Fu

J. Mar. Sci. Eng. 2024, 12(5), 694; https://doi.org/10.3390/jmse12050694 - 23 Apr 2024

Viewed by 639

Abstract

Aiming at the problem of hovercraft formation–containment control with compound perturbations including model uncertainties and ocean disturbances, a distributed control algorithm for underactuated hovercraft formation–containment is proposed by combining adaptive linear extended state observer (ALESO) and radial basis function neural network (RBFNN). Firstly, ALESO and RBFNN are designed to estimate the ocean disturbances and model uncertainties, respectively, for dynamic compensation in the controller. Then, the auxiliary variables are introduced into the formation error function, and the lateral and longitudinal error stabilization is transformed into the design of longitudinal force and rotational torque by using the skew-symmetric matrix transformation, which solves the lateral underactuated problem of the hovercraft. Finally, the uniform ultimate boundedness of formation–containment cooperative errors is proved by the Lyapunov stability theory. Digital simulation verifies the effectiveness of the proposed method. Full article

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

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Review

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29 pages, 6845 KiB

Open AccessReview

A Review of Autonomous Berthing Technology for Ships

by Jiangliu Cai, Guoquan Chen, Jian Yin, Chong Ding, Yongfeng Suo and Jinhai Chen

J. Mar. Sci. Eng. 2024, 12(7), 1137; https://doi.org/10.3390/jmse12071137 - 6 Jul 2024

Viewed by 759

Abstract

Autonomous berthing technology is a crucial engineering control problem within the ship intelligence system, encompassing a series of complex operations and technologies. Firstly, this paper analyses the research on autonomous berthing technology from a bibliometric point of view in order to obtain an overview of its past and present development and to outline the importance of this technology. Secondly, a literature review is conducted on each of the four aspects of autonomous berthing technology, namely sensing technology, berthing type, control method, and evaluation method, which can help to quickly understand the main aspects of this technology. Thirdly, the ship-assisting technologies needed to achieve autonomous berthing are discussed and analysed from six aspects: dynamic collision avoidance, path planning, path tracking, heading control, tug assistance, and shore-based systems. Finally, the challenges faced by the ship autonomous berthing technology on the way of development are summarised, and future development is projected. This paper aims to provide a more comprehensive perspective for analysing and researching ship autonomous berthing technology. Full article

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

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