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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 (15 January 2024) | Viewed by 17639

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Special Issue Editors

Dr. Haitong Xu

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Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: autonomous ships; guidance navigation and control; nonlinear control; ship manoeuvering model; system identification method; full-scale trials and model tests
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Dr. Lúcia Moreira

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Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: autonomous vehicles; guidance, navigation and control; ship dynamics; artificial intelligence in maritime applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xianbo Xiang

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Guest Editor

School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: marine robotics; modeling, sensing, navigation, guidance, control and coordination of ships and marine vehicles; autonomous surface vehicle; autonomous underwater vehicle; intelligent marine systems

Dr. Carlos Guedes Soares

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Guest Editor

Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: marine environment; ship dynamics; marine structures; safety and reliability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the increasingly strict environmental and safety regulations becoming and issues of crew safety, the maritime industry is being confronted with a range of pressing challenges. Given this, the use of autonomous ships provides potential solutions to respond to challenges, such as greenhouse gas (GHG) emissions, fuel savings and safety. The development towards marine autonomy technology will significantly improve the situation and is expected to become a cost-efficient alternative to conventional ships. Currently, automated shipping technology is rapidly transitioning from theoretical to practical applications as the number and scope of autonomous ship prototypes increase around the globe. They are widely used in both navy and commercial applications, such as ocean observer, coast patrol, underwater monitoring and underwater production system operation, to name just a few.

The main goal of this Special Issue is to address the key challenges, thereby promoting research on maritime autonomous ships. The topics of interest in this Special Issue include, but are not limited to, the following:

Intelligent and autonomous ships;
Autonomous maritime operations;
Maritime control systems and applications;
Automatic berthing and unberthing;
Automated onboard systems;
Shore control centre;
Remote operations;
Multi-objective optimization design;
Automatic identification system (AIS);
Situational awareness;
Decision making and logic;
Sea trials and ship model tests;
Data acquisition systems and multi-sensor data fusion;
Safety and risk assessment for autonomous ships and respective regulations;
Machine learning methods and their application in maritime autonomous ships.

Dr. Haitong Xu
Dr. Lúcia Moreira
Prof. Dr. Xianbo Xiang
Dr. Carlos Guedes Soares
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 and autonomous ships
ship dynamics
guidance, navigation and control systems
seakeeping model
machine learning methods
safety and risk
ship model tests
captive model test
automatic collision avoidance
path planning
automated onboard systems

Published Papers (10 papers)

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Research

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21 pages, 3341 KiB

Open AccessArticle

A Novel, Finite-Time, Active Fault-Tolerant Control Framework for Autonomous Surface Vehicle with Guaranteed Performance

by Xuerao Wang, Yuncheng Ouyang, Xiao Wang and Qingling Wang

J. Mar. Sci. Eng. 2024, 12(2), 347; https://doi.org/10.3390/jmse12020347 - 17 Feb 2024

Viewed by 568

Abstract

In this paper, a finite-time, active fault-tolerant control (AFTC) scheme is proposed for a class of autonomous surface vehicles (ASVs) with component faults. The designed AFTC framework is based on an integrated design of fault detection (FD), fault estimation (FE), and controller reconfiguration. First, a nominal controller based on the Barrier Lyapunov function is presented, which guarantees that the tracking error converges to the predefined performance constraints within a settling time. Then, a performance-based monitoring function with low complexity is designed to supervise the tracking behaviors and detect the fault. Different from existing results where the fault is bounded by a known scalar, the FE in this study is implemented by a finite-time estimator without requiring any prioir information of fault. Furthermore, under the proposed finite-time AFTC scheme, both the transient and steady-state performance of the ASV can be guaranteed regardless of the occurrence of faults. Finally, a simulation example on CyberShip II is given to confirm the effectiveness of the proposed AFTC method. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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21 pages, 11521 KiB

Open AccessArticle

Collision-Free Formation-Containment Tracking of Multi-USV Systems with Constrained Velocity and Driving Force

by Jingchen Wang, Qihe Shan, Tieshan Li, Geyang Xiao and Qi Xu

J. Mar. Sci. Eng. 2024, 12(2), 304; https://doi.org/10.3390/jmse12020304 - 09 Feb 2024

Cited by 1 | Viewed by 629

Abstract

This paper studied the collision avoidance issue in the formation-containment tracking control of multi-USVs (unmanned surface vehicles) with constrained velocity and driving force. Specifically, based on a dual-layer control framework, it designed a multi-USV formation-containment tracking control strategy that accounts for constrained motion velocity and input driving force and validated the stability of this strategy using the Lyapunov method. Then, by utilizing zeroing control barrier function certificates, it considered collision avoidance among USVs with various roles as well as between each USV and static obstacles. A collision-free multi-USV formation-containment tracking control strategy considering constrained motion velocity and driving force was thus established, and its effectiveness was validated through the proposed simulation. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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22 pages, 3898 KiB

Open AccessArticle

Formation Control for UAV-USVs Heterogeneous System with Collision Avoidance Performance

by Yuyang Huang, Wei Li, Jun Ning and Zhihui Li

J. Mar. Sci. Eng. 2023, 11(12), 2332; https://doi.org/10.3390/jmse11122332 - 10 Dec 2023

Viewed by 850

Abstract

This paper investigates the cooperative formation trajectory tracking problem for heterogeneous unmanned aerial vehicle (UAV) and multiple unmanned surface vessel (USV) systems with collision avoidance performance. Firstly, a formation control protocol based on extended state observer (ESO) is proposed to ensure that the UAV and the USVs track the target trajectory simultaneously in the XY plane. Then, the collision avoidance control strategy of USV formation based on artificial potential field (APF) theory is designed. Specifically, the APF method is improved by reconstructing the repulsive potential field to make the collision avoidance action of USVs more in line with the requirements of International Regulations for Preventing Collisions at Sea (COLREGs). Following that, an altitude controller for the UAV is proposed to maintain the cooperative formation of the heterogeneous systems. Based on the input-to-state stability, the stability of the proposed control structure is proven, and all the signals in the closed-loop system are ultimately bounded. Finally, a simulation study is provided to show the efficacy of the proposed strategy. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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37 pages, 23783 KiB

Open AccessArticle

A Multi-Ship Collision Avoidance Algorithm Using Data-Driven Multi-Agent Deep Reinforcement Learning

by Yihan Niu, Feixiang Zhu, Moxuan Wei, Yifan Du and Pengyu Zhai

J. Mar. Sci. Eng. 2023, 11(11), 2101; https://doi.org/10.3390/jmse11112101 - 01 Nov 2023

Cited by 3 | Viewed by 1155

Abstract

Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The use of intelligent technology to achieve autonomous collision avoidance is a hot issue widely discussed in the industry. In the endeavor to solve this problem, multi-ship coordinated collision avoidance has become a crucial challenge. This paper proposes a multi-ship autonomous collision avoidance decision-making algorithm by a data-driven method and adopts the Multi-agent Deep Reinforcement Learning (MADRL) framework for its design. Firstly, the overall framework of this paper and its components follow the principle of “reality as primary and simulation as supplementary”, so a real data-driven AIS (Automatic Identification System) dominates the model construction. Secondly, the agent’s observation state is determined by quantifying the hazardous area. Then, based on a full understanding of the International Regulations for Preventing Collisions at Sea (COLREGs) and the preliminary data collection, this paper combines the statistical results of the real water traffic data to guide and design the algorithm framework and selects the representative influencing factors to be designed in the collision avoidance decision-making algorithm’s reward function. Next, we train the algorithmic model using both real data and simulation data. Meanwhile, Prioritized Experience Replay (PER) is adopted to accelerate the model’s learning efficiency. Finally, 40 encounter scenarios are designed and extended to verify the algorithm performance based on the idea of the Imazu problem. The experimental results show that this algorithm can efficiently make a ship collision avoidance decision in compliance with COLREGs. Multi-agent learning through shared network policies can ensure that the agents pass beyond the safe distance in unknown environments. We can apply the trained model to the system with different numbers of agents to provide a reference for the research of autonomous collision avoidance in ships. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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19 pages, 6242 KiB

Open AccessArticle

Development of a Graph-Based Collision Risk Situation Model for Validation of Autonomous Ships’ Collision Avoidance Systems

by Taewoong Hwang and Ik-Hyun Youn

J. Mar. Sci. Eng. 2023, 11(11), 2037; https://doi.org/10.3390/jmse11112037 - 24 Oct 2023

Cited by 1 | Viewed by 933

Abstract

In the maritime industry, the systematic validation of collision avoidance systems of autonomous ships is becoming an increasingly important issue with the development of autonomous ships. The development of collision avoidance systems for autonomous ships faces inherent risks of programming errors and has mostly been tested in limited scenarios. Despite efforts to verify these systems through scenario testing, these scenarios do not fully represent the complex nature of real-world navigation, limiting full system verification and reliability. Therefore, this study proposed a method for analyzing collision risk situations extracted from AIS data through graph-based modeling and establishing validation scenarios. This methodology categorizes collision risk scenarios according to their centrality and frequency and demonstrates how simple collision risk situations gradually evolve into harsh situations. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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17 pages, 1205 KiB

Open AccessArticle

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

by Muhammad Yasir, Abdoul Jelil Niang, Md Sakaouth Hossain, Qamar Ul Islam, Qian Yang and Yuhang Yin

J. Mar. Sci. Eng. 2023, 11(10), 1916; https://doi.org/10.3390/jmse11101916 - 04 Oct 2023

Cited by 2 | Viewed by 2006

Abstract

We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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16 pages, 4127 KiB

Open AccessArticle

Data-Driven Parameter Estimation of Nonlinear Ship Manoeuvring Model in Shallow Water Using Truncated Least Squares Support Vector Machines

by Haitong Xu and C. Guedes Soares

J. Mar. Sci. Eng. 2023, 11(10), 1865; https://doi.org/10.3390/jmse11101865 - 26 Sep 2023

Viewed by 1057

Abstract

A data-driven method, the truncated LS-SVM, is proposed for estimating the nondimensional hydrodynamic coefficients of a nonlinear manoeuvring model. Experimental data collected in a shallow water towing tank are utilized in this study. To assess the accuracy and robustness of the truncated LS-SVM method, different test data sizes are selected as the training set. The identified nondimensional hydrodynamic coefficients are presented, as well as the corresponding parameter uncertainty and confidence intervals. The validation is carried out using the reference data, and statistical measures, such as the correlation coefficient, centred RMS difference, and standard deviation are employed to quantify the similarity. The results demonstrate that the truncated LS-SVM method effectively models the hydrodynamic force prediction problems with a large training set, reducing parameter uncertainty and yielding more convincing results. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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22 pages, 24823 KiB

Open AccessArticle

Attitude Estimation Method for Target Ships Based on LiDAR Point Clouds via An Improved RANSAC

by Shengzhe Wei, Yuminghao Xiao, Xinde Yang and Hongdong Wang

J. Mar. Sci. Eng. 2023, 11(9), 1755; https://doi.org/10.3390/jmse11091755 - 08 Sep 2023

Cited by 1 | Viewed by 975

Abstract

The accurate attitude estimation of target ships plays a vital role in ensuring the safety of marine transportation, especially for tugs. A Light Detection and Ranging (LiDAR) system can generate 3D point clouds to describe the target ship’s geometric features that possess attitude information. In this work, the authors put forward a new attitude-estimation framework that first extracts the geometric features (i.e., the board-side plane of a ship) using point clouds from shipborne LiDAR and then computes the attitude that is of interest (i.e., yaw and roll in this paper). To extract the board-side plane accurately on a moving ship with sparse point clouds, an improved Random Sample Consensus (RANSAC) algorithm with a pre-processing normal vector-based filter was designed to exclude noise points. A real water-pool experiment and two numerical tests were carried out to demonstrate the accuracy and general applicability of the attitude estimation of target ships brought by the improved RANSAC and estimation framework. The experimental results show that the average mean absolute errors of the angle and angular-rate estimation are 0.4879 deg and 4.2197 deg/s, respectively, which are 92.93% and 75.36% more accurate than the estimation based on standard RANSAC. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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20 pages, 5380 KiB

Open AccessArticle

Model Experimental Study on a T-Foil Control Method with Anti-Vertical Motion Optimization of the Mono Hull

by Yifang Sun, Dapeng Zhang, Yiqun Wang, Zhi Zong and Zongduo Wu

J. Mar. Sci. Eng. 2023, 11(8), 1551; https://doi.org/10.3390/jmse11081551 - 04 Aug 2023

Cited by 1 | Viewed by 781

Abstract

T-foils with active control systems can adjust their attack angle according to the movement of the ship in real time, providing higher lift force and improving the seakeeping performance of a ship. The optimization of the control signal and that of the control method have an important influence on the effect of active T-foils. In this paper, the control method of the T-foil’s swinging angle is established and optimized on the basis of model testing in order to increase the effect of the T-foil. First, the governing equation is introduced by establishing the proportional relationship between the angular motion of the hull and the lift moment of the T-foil. On the basis of the model of the T-foil’s lift force, the governing equation of the T-foil’s swinging angle is deduced and simplified using the test results of the ship model with a passive T-foil and without a T-foil. Then, the active T-foil control system is established by comparing the effects of T-foils with different control signals. Finally, the efficacies of the passive and active T-foil are reported and discussed. It is found that the pitch angular velocity is a more appropriate signal than the pitch angle and pitch angular acceleration. T-foils with pitch angular velocity control can decrease the vertical motion response in the resonance region of a ship’s encounter frequency by more than about 20% compared to the case of the bare ship model, while also increasing the anti-bow acceleration effect by more than 15% compared to the case of passive control. The results obtained by model testing have a certain guiding significance for specific engineering practices. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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Review

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31 pages, 7824 KiB

Open AccessEditor’s ChoiceReview

Digital Twin in the Maritime Domain: A Review and Emerging Trends

by Nuwan Sri Madusanka, Yijie Fan, Shaolong Yang and Xianbo Xiang

J. Mar. Sci. Eng. 2023, 11(5), 1021; https://doi.org/10.3390/jmse11051021 - 10 May 2023

Cited by 10 | Viewed by 6497

Abstract

This paper highlights the development of Digital Twin (DT) technology and its admittance to a variety of applications within the maritime domain in general and surface ships in particular. The conceptual theory behind the evolution of DT is highlighted along with the development of the technology and current progress in practical applications with an exploration of the key milestones in the extension from the electrification of the shipping sector towards the realization of a definitive DT-based system. Existing DT-based applications within the maritime sector are surveyed along with the comprehension of ongoing research work. The development strategy for a formidable DT architecture is discussed, culminating in a proposal of a four-layered DT framework. Considering the importance of DT, an extensive and methodical literature survey has also been carried out, along with a comprehensive scientometric analysis to unveil the methodical footprint of DT in the marine sector, thus leading the way for future work on the design, development and operation of surface vessels using DT applications. Full article

(This article belongs to the Special Issue Maritime Autonomous Surface Ships)

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