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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 (1 August 2023) | Viewed by 26364

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Prof. Dr. Xiao Liang

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Guest Editor

School of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, China
Interests: decision-making and advanced control; unmanned technology and swarm intelligence in maritime applications; autonomous surface vehicles; autonomous underwater vehicles
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rubo Zhang

E-Mail Website
Guest Editor

College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, China
Interests: intelligent robot hardware and software architecture; task planning; path planning; multi-robot technology; autonomous decision-making technology in complex environments
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Dr. Xingru Qu

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Guest Editor

School of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, China
Interests: autonomous marine vehicles (underwater and surface); guidance and control; coordination
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been growing progress in autonomous marine vehicle operations, motivated by the impact on increasingly challenging scientific, economical, and environmental surface and underwater applications. The increasing demand for marine vehicles to undertake more complex tasks under the harsh marine environments poses new challenges which necessitate the development of advanced and intelligent operation approaches for single and multiple vehicles. This Special issue offers a collection of high-quality research articles contributing to topics on:

Marine vehicle modelling and simulation technologies;
Marine vehicle navigation and guidance;
Path planning, motion control, obstacle detection, and avoidance;
Learning-based control algorithms for marine systems;
Swarm intelligence and control in marine vehicle applications;
Distributed and cooperative marine vehicles and systems;
Underwater vision and identification;
Water surface object detection and recognition;
Fault diagnosis design and fault tolerant control of ROV/UUV;
Propulsion systems and energy efficiency;
Maritime safety and risk assessment.

Prof. Dr. Xiao Liang
Prof. Dr. Rubo Zhang
Dr. Xingru Qu
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

autonomous operations
surface and underwater applications
navigation and guidance
swarm control
detection and recognition
safety and efficiency

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Published Papers (13 papers)

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Editorial

Jump to: Research

5 pages, 163 KiB

Open AccessEditorial

Autonomous Marine Vehicle Operations

by Xiao Liang, Rubo Zhang and Xingru Qu

J. Mar. Sci. Eng. 2024, 12(2), 355; https://doi.org/10.3390/jmse12020355 - 19 Feb 2024

Viewed by 1442

Abstract

The world has witnessed the rapid development of autonomous marine vehicles,
such as surface vehicles and underwater vehicles, which have created fruitful innovative approaches to previously unsolvable problems in marine and ocean engineering [...] Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

Research

Jump to: Editorial

16 pages, 3170 KiB

Open AccessArticle

A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

by Jiannan Zhu, Yixin Yang and Yuwei Cheng

J. Mar. Sci. Eng. 2023, 11(9), 1794; https://doi.org/10.3390/jmse11091794 - 14 Sep 2023

Cited by 4 | Viewed by 2195

Abstract

Unmanned surface vehicles (USVs) have wide applications in marine inspection and monitoring, terrain mapping, and water surface cleaning. Accurate and robust environment perception ability is essential for achieving autonomy in USVs. Small object detection on water surfaces is an important environment perception task, typically achieved by visual detection using cameras. However, existing vision-based small object detection methods suffer from performance degradation in complex water surface environments. Therefore, in this paper, we propose a millimeter-wave (mmWave) radar-aided vision detection method that enables automatic data association and fusion between mmWave radar point clouds and images. Through testing on real-world data, the proposed method demonstrates significant performance improvement over vision-based object detection methods without introducing more computational costs, making it suitable for real-time application on USVs. Furthermore, the image–radar data association model in the proposed method can serve as a plug-and-play module for other object detection methods. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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21 pages, 52600 KiB

Open AccessArticle

The Hydrodynamic Interaction between an AUV and Submarine during the Recovery Process

by Wanzhen Luo, Caipeng Ma, Dapeng Jiang, Tiedong Zhang and Tiecheng Wu

J. Mar. Sci. Eng. 2023, 11(9), 1789; https://doi.org/10.3390/jmse11091789 - 13 Sep 2023

Cited by 3 | Viewed by 1809

Abstract

The hydrodynamic interaction between an AUV (Autonomous Underwater Vehicle) and a recovery device, such as a real-scale submarine, is a crucial factor affecting the safe recovery of the AUV. This paper employs the CFD (Computational Fluid Dynamics) method to investigate the hydrodynamic interaction of the AUV and the submarine during the recovery process. Both the AUV and the submarine are considered to be relatively stationary. The results indicate that the submarine has a significant impact on the AUV during the recovery process, with sailing speed and relative positions identified as key influential factors. Due to the influence of the submarine, it can be difficult for the AUV to approach the submarine and be recovered safely. This study provides valuable insights into the hydrodynamic interaction between the AUV and the recovery device, and offers guidance for future submarine recovery operations involving AUVs. By considering the influence of the submarine’s position and motion, as well as other relevant factors, it may be possible to improve the stability, safety, and efficiency of AUV recovery operations. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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15 pages, 2828 KiB

Open AccessArticle

A Deep Reinforcement Learning-Based Path-Following Control Scheme for an Uncertain Under-Actuated Autonomous Marine Vehicle

by Xingru Qu, Yuze Jiang, Rubo Zhang and Feifei Long

J. Mar. Sci. Eng. 2023, 11(9), 1762; https://doi.org/10.3390/jmse11091762 - 9 Sep 2023

Cited by 4 | Viewed by 1668

Abstract

In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight guidance, a surge-heading joint guidance method is developed within the kinematic level, thereby enabling the AMV to follow the desired path accurately. Within the dynamic level, model uncertainties and time-varying environment disturbances are taken into account, and the reinforcement learning control method using the twin-delay deep deterministic policy gradient (TD3) is developed for the under-actuated vehicle, where path-following actions are generated via the state space and hybrid rewards. Additionally, actor-critic networks are developed using the long-short time memory (LSTM) network, and the vehicle can successfully make a decision by the aid of historical states, thus enhancing the convergence rate of dynamic controllers. Simulation results and comprehensive comparisons on a prototype AMV demonstrate the remarkable effectiveness and superiority of the proposed LSTM-TD3-based path-following control scheme. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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24 pages, 3549 KiB

Open AccessArticle

Dynamic Data-Driven Application System for Flow Field Prediction with Autonomous Marine Vehicles

by Qianlong Jin, Yu Tian, Weicong Zhan, Qiming Sang, Jiancheng Yu and Xiaohui Wang

J. Mar. Sci. Eng. 2023, 11(8), 1617; https://doi.org/10.3390/jmse11081617 - 18 Aug 2023

Cited by 1 | Viewed by 1407

Abstract

Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-time data assimilation from flow sensing, and the optimization of AMVs’ sensing strategies, culminating in a closed-loop dynamic data-driven application system (DDDAS). This article presents a novel DDDAS that systematically integrates flow modeling, data assimilation, and adaptive flow sensing using networked AMVs. It features a hybrid data-driven flow model, uniting a neural network for trend prediction and a Gaussian process model for residual fitting. The neural network architecture is designed using knowledge extracted from historic flow data through tidal harmonic analysis, enhancing its capability in flow prediction. The Kriged ensemble transform Kalman filter is introduced to assimilate spatially correlated flow-sensing data from AMVs, enabling effective model learning and accurate spatiotemporal flow prediction, while forming the basis for optimizing AMVs’ flow-sensing paths. A receding horizon strategy is proposed to implement non-myopic optimal path planning, and a distributed strategy of implementing Monte Carlo tree search is proposed to solve the resulting large-scale tree searching-based optimization problem. Computer simulations, employing underwater gliders as sensing networks, demonstrate the effectiveness of the proposed DDDAS in predicting depth-averaged flow in nearshore ocean environments. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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18 pages, 6607 KiB

Open AccessArticle

Critical Node Identification of Multi-UUV Formation Based on Network Structure Entropy

by Yi Chen, Lu Liu, Xiaomeng Zhang, Wei Qiao, Ranzhen Ren, Boyu Zhu, Lichuan Zhang, Guang Pan and Yang Yu

J. Mar. Sci. Eng. 2023, 11(8), 1538; https://doi.org/10.3390/jmse11081538 - 1 Aug 2023

Cited by 1 | Viewed by 1309

Abstract

In order to identify and attack the multi-UUV (unmanned underwater vehicle) groups, this paper proposes a method for identifying the critical nodes of multi-UUV formations. This method helps in combating multi-UUV formations by identifying the key nodes to attack them. Moreover, these multi-UUV formations are considered to have an unknown structure as the research object. Therefore, the network structure of the formation is reconstructed according to its space–time trajectory, and the importance of nodes is determined based on network structure entropy. As for the methodology, firstly, based on the swarm intelligence behavior method, the motion similarity of multi-UUV nodes in the formation is analyzed in pairs; furthermore, the leader–follower relationship and the network structure of the formation are calculated successively. Then, based on this network structure, the importance of the network nodes is further determined by the network structure entropy method. Finally, through simulation and experiments, it is verified that the algorithm can accurately construct the network structure of the unknown multi-UUV formation, and the accuracy of the calculated time delay data reaches 84.6%, and compared with the traditional information entropy algorithm, the ordering of the important nodes obtained by this algorithm is more in line with the underwater formation network. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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14 pages, 20200 KiB

Open AccessArticle

Analysis of the Steady-Stream Active Flow Control for the Blended-Winged-Body Underwater Glider

by Xiaoxu Du, Xin Liu and Yani Song

J. Mar. Sci. Eng. 2023, 11(7), 1344; https://doi.org/10.3390/jmse11071344 - 30 Jun 2023

Cited by 4 | Viewed by 1744

Abstract

The BWB-UG is a glider with a smooth and integrated fuselage and wing. Its lift-to-drag properties are some of the most significant factors affecting its performance. In order to improve its hydrodynamic characteristics, the method of steady-stream active flow control (SS-AFC) is proposed. The computational fluid dynamics method is used to numerically investigate the SS-AFC of the BWB-UG. The mechanism of the SS-AFC effect on the lift-to-drag characteristics is revealed from the flow field aspect. The flow field of the BWB-UG before and after installing the SS-AFC was simulated using FLUENT. The results show that the SS-AFC can effectively optimise the hydrodynamic characteristics of the BWB-UG and can optimise the structure of the flow field around the BWB-UG. The steady-suction AFC can increase the lift-to-drag ratio of the BWB-UG by up to

45.01 %

. With the steady-jet AFC, the lift-to-drag ratio of the BWB-UG can be increased by as much as

93.17 %

. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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14 pages, 2423 KiB

Open AccessArticle

Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

by Yan Sun, Wenxi Zheng, Xue Du and Zheping Yan

J. Mar. Sci. Eng. 2023, 11(6), 1178; https://doi.org/10.3390/jmse11061178 - 5 Jun 2023

Cited by 16 | Viewed by 2419

Abstract

The underwater imaging environment is complex, and the application of conventional target detection algorithms to the underwater environment has yet to provide satisfactory results. Therefore, underwater optical image target detection remains one of the most challenging tasks involved with neighborhood-based techniques in the field of computer vision. Small underwater targets, dispersion, and sources of distortion (such as sediment and particles) often render neighborhood-based techniques insufficient, as existing target detection algorithms primarily focus on improving detection accuracy and enhancing algorithm complexity and computing power. However, excessive extraction of deep-level features leads to the loss of small targets and decrease in detection accuracy. Moreover, most underwater optical image target detection is performed by underwater unmanned platforms, which have a high demand of algorithm lightweight requirements due to the limited computing power of the underwater unmanned platform with the mobile vision processing platform. In order to meet the lightweight requirements of the underwater unmanned platform without affecting the detection accuracy of the target, we propose an underwater target detection model based on mobile vision transformer (MobileViT) and YOLOX, and we design a new coordinate attention (CA) mechanism named a double CA (DCA) mechanism. This model utilizes MobileViT as the algorithm backbone network, improving the global feature extraction ability of the algorithm and reducing the amount of algorithm parameters. The double CA (DCA) mechanism can improve the extraction of shallow features as well as the detection accuracy, even for difficult targets, using a minimum of parameters. Research validated in the Underwater Robot Professional Contest 2020 (URPC2020) dataset revealed that this method has an average accuracy rate of 72.00%. In addition, YOLOX’s ability to compress the model parameters by 49.6% efficiently achieves a balance between underwater optical image detection accuracy and parameter quantity. Compared with the existing algorithm, the proposed algorithm can carry on the underwater unmanned platform better. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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21 pages, 3839 KiB

Open AccessArticle

Physical Consistent Path Planning for Unmanned Surface Vehicles under Complex Marine Environment

by Fang Wang, Yong Bai and Liang Zhao

J. Mar. Sci. Eng. 2023, 11(6), 1164; https://doi.org/10.3390/jmse11061164 - 1 Jun 2023

Cited by 4 | Viewed by 1936

Abstract

The increasing demand for safe and efficient maritime transportation has underscored the necessity of developing effective path-planning algorithms for Unmanned Surface Vehicles (USVs). However, the inherent complexities of the ocean environment and the non-holonomic properties of the physical system have posed significant challenges to designing feasible paths for USVs. To address these issues, a novel path planning framework is elaborately designed, which consists of an optimization model, a meta-heuristic solver, and a Clothoid-based path connector. First, by encapsulating the intricate nature of the ocean environment and ship dynamics, a multi-objective path planning problem is designed, providing a comprehensive and in-depth portrayal of the underlying mechanism. By integrating the principles of the candidate set random testing initialization and adaptive probability set, an enhanced genetic algorithm is devised to fully exploit the underlying optimization problem in constrained space, contributing to the global searching ability. Accounting for the non-holonomic constraints, the fast-discrete Clothoid curve is capable of maintaining and improving the continuity of the path curve, thereby promoting strong coordination between the planning and control modules. A thorough series of simulations and comparisons conducted in diverse ocean scenarios has conclusively demonstrated the effectiveness and superiority of the proposed path planning framework. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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20 pages, 116827 KiB

Open AccessArticle

Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

by Aidi Yu, Yujia Wang, Haoyuan Li and Boyang Qiu

J. Mar. Sci. Eng. 2023, 11(6), 1140; https://doi.org/10.3390/jmse11061140 - 29 May 2023

Cited by 4 | Viewed by 1514

Abstract

To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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21 pages, 11672 KiB

Open AccessArticle

Collision Avoidance Strategy for Unmanned Surface Vessel Considering Actuator Faults Using Kinodynamic Rapidly Exploring Random Tree-Smart and Radial Basis Function Neural Network-Based Model Predictive Control

by Yunxuan Song, Yimin Chen, Jian Gao, Yazhou Wang and Guang Pan

J. Mar. Sci. Eng. 2023, 11(6), 1107; https://doi.org/10.3390/jmse11061107 - 23 May 2023

Cited by 5 | Viewed by 1745

Abstract

Path planning and tracking are essential technologies for unmanned surface vessels (USVs). The kinodynamic constraints and actuator faults, however, bring difficulties in finding feasible paths and control efforts. This paper proposes a collision avoidance strategy for USV by developing the kinodynamic rapidly exploring random tree-smart (kinodynamic RRT*-smart) algorithm and the fault-tolerant control method. By utilizing the triangular inequality and the intelligent biased sampling strategy, the kinodynamic RRT*-smart shows its advantages in terms of path length, cost and running time. With consideration of kinodynamic constraints, a feasible and collision-free trajectory can be provided. Then, a radial basis function neural network-based model predictive control (RBF-MPC) method was designed that compensates for the model’s uncertainties by developing the radial basis function neural network (RBF-NN) approximator and by constructing a feedback-state training dataset in real time. Furthermore, two types of fault situation were analyzed considering the thruster failure. We established the faults’ mathematical models and investigated the fault-tolerant strategies for different fault types. The simulation studies were conducted to validate the effectiveness of the proposed strategy. The results show that the proposed planning and control methods can avoid obstacles in faulty conditions. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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15 pages, 4959 KiB

Open AccessArticle

An Accurate Detection Model of Takifugu rubripes Using an Improved YOLO-V7 Network

by Siyi Zhou, Kewei Cai, Yanhong Feng, Xiaomeng Tang, Hongshuai Pang, Jiaqi He and Xiang Shi

J. Mar. Sci. Eng. 2023, 11(5), 1051; https://doi.org/10.3390/jmse11051051 - 15 May 2023

Cited by 22 | Viewed by 3655

Abstract

In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. However, the underwater environment is complex and affected by lighting, water quality, and the mutual obscuration of fish bodies. Therefore, underwater fish images are not very clear, which restricts the recognition accuracy of underwater targets. This paper proposes an improved YOLO-V7 model for the identification of Takifugu rubripes. Its specific implementation methods are as follows: (1) The feature extraction capability of the original network is improved by adding a sizeable convolutional kernel model into the backbone network. (2) Through ameliorating the original detection head, the information flow forms a cascade effect to effectively solve the multi-scale problems and inadequate information extraction of small targets. (3) Finally, this paper appropriately prunes the network to reduce the total computation of the model; meanwhile, it ensures the precision of the detection. The experimental results show that the detection accuracy of the improved YOLO-V7 model is better than that of the original. The average precision improved from 87.79% to 92.86% (when the intersection over union was 0.5), with an increase of 5.07%. Additionally, the amount of computation was reduced by approximately 35%. This shows that the detection precision of the proposed network model was higher than that for the original model, which can provide a reference for the intelligent aquaculture of fishes. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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24 pages, 7245 KiB

Open AccessArticle

An Improved S-Plane Controller for High-Speed Multi-Purpose AUVs with Situational Static Loads

by Chunmeng Jiang, Jinhua Lv, Lei Wan, Jianguo Wang, Bin He and Gongxing Wu

J. Mar. Sci. Eng. 2023, 11(3), 646; https://doi.org/10.3390/jmse11030646 - 19 Mar 2023

Cited by 5 | Viewed by 1745

Abstract

The classic S-plane control method combines PD structure with fuzzy control theory, with the advantages of a simple control structure and fewer parameters to be adjusted. It has been proved as a practical method in an autonomous underwater vehicle (AUV) motion control at low and medium speeds, but it takes no account of the situational static load and varying hydrodynamic forces which influence the control quality and even result in a “dolphin effect” at the time of high-speed movement. For this reason, an improved S-plane controller is designed based on the sliding mode variable structure, sliding mode surface, and control items in order to respond to the situational static load and high-speed movement. The improved S-plane controller is verified by Lyapunov stability analysis. The thrust allocation strategies are also discussed with constraints introduced in accordance with task requirements. In order to verify the practicability and effectiveness of the improved S-plane controller, both simulation experiments and field trials of AUV motion control, long-range cruise, and path point following were carried out. The results have demonstrated the superiority of the improved S-plane controller over the classic S-plane controller. Full article

(This article belongs to the Special Issue Autonomous Marine Vehicle Operations)

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