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Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 10298

Special Issue Editors

Dr. Liying Hao

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: robust fault tolerant control; intelligent control; deep learning; unmanned marine vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Zuo

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China; The Research Institute for Socionetwork Strategies, Kansai University, Osaka 5648680, Japan
Interests: evolutionary computation; machine learning; data science; social computing
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Li

E-Mail Website
Guest Editor
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: machine learning; image processing; software test; big data

Special Issue Information

Dear Colleagues,

Unmanned marine vehicles (UMVs) is a collective term used to describe autonomous underwater vehicles, remotely operated vehicles, semi-submersibles, and unmanned surface craft. Worldwide interest in the design and development of UMVs is rapidly increasing as they are now considered by many as being able to provide cost-effective solutions to a number of commercial, naval and scientific problems. This worldwide interest in such vehicles is combined with the current and ongoing advances being made in control systems engineering, artificial intelligence and Big Data technology.

The development of intelligence control methods and marine applications is related to the fact that there is a large amount of available data and knowledge on such methods. The application of intelligence control in practice is conditioned by prerequisites that must be met for the effective development, deployment, and use of such solutions in the context of sustainable development. In addition to the mentioned availability of suitable marine Big Data and intelligent control techniques, it is also a process that supports the implementation of innovative projects.

This Special Issue aims to discuss new applications of unmanned marine vehicles and intelligent control methods in various industrial and research solutions, contributing to the sustainable development of processes, focusing on applications in marine vehicles and other industrial technologies and presenting strengths and weaknesses, but also opportunities and limitations from their application with regard to sustainable development. This Special Issue will provide an opportunity for academia and the industry to present innovative solutions for the application of marine vehicles, intelligent control methods and Big Data processing technologies for sustainability.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Unmanned marine vehicles (UMVs);
  • Unmanned surface vehicles (USVs);
  • Unmanned underwater vehicles (UUVs);
  • Remotely operated vehicles (ROVs);
  • Data sciences;
  • Deep learning;
  • Reinforcement learning;
  • Intelligent control technology;
  • Optimization;
  • Big Data;
  • Control theory and applications.

We look forward to receiving your contributions.

Dr. Liying Hao
Dr. Yi Zuo
Dr. Hui Li
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. Sustainability 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 2400 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

  • unmanned marine vehicles (UMVs)
  • data sciences
  • control theory and applications
  • artificial intelligence
  • intelligent control

Published Papers (6 papers)

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Research

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31 pages, 16213 KiB  
Article
Prediction of Ship Trajectory in Nearby Port Waters Based on Attention Mechanism Model
by Junhao Jiang and Yi Zuo
Sustainability 2023, 15(9), 7435; https://doi.org/10.3390/su15097435 - 30 Apr 2023
Cited by 5 | Viewed by 1347
Abstract
In recent years, the prediction of ship trajectory based on automatic identification system (AIS) data has become an important area of research. Among the existing studies, most focus on a single ship to extract features and train models for trajectory prediction. However, in [...] Read more.
In recent years, the prediction of ship trajectory based on automatic identification system (AIS) data has become an important area of research. Among the existing studies, most focus on a single ship to extract features and train models for trajectory prediction. However, in a real situation, AIS contains a variety of ships and trajectories that need a general model to serve various cases. Therefore, in this paper, we include an attentional mechanism to train a multi-trajectory prediction model. There are three major processes in our model. Firstly, we improve the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm and apply it to trajectory clustering. According to the clustering process, ship trajectories can be automatically separated by groups. Secondly, we propose a feature extraction method based on a hierarchical clustering method for a trajectory group. According to the extraction process, typical trajectories can be obtained for individual groups. Thirdly, we propose a multi-trajectory prediction model based on an attentional mechanism. The proposed model was trained using typical trajectories and tested using original trajectories. In the experiments, we chose nearby port waters as the target, which contain various ships and trajectories, to validate our model. The experimental results show that the mean absolute errors (MAEs) of the model in longitude (°) and latitude (°) compared with the baseline methods were reduced by 8.69% and 6.12%. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry)
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14 pages, 2272 KiB  
Article
The Autonomous Intersection Control Method Based on Reduction in Vehicle Conflict Relationships
by Mingjian Liu, Chao Zheng and Yunhe Zhu
Sustainability 2023, 15(9), 7142; https://doi.org/10.3390/su15097142 - 24 Apr 2023
Viewed by 1100
Abstract
Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this [...] Read more.
Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this study. First, a conflict relationship graph that can describe the driving conflict relationship between vehicles is constructed. Second, the complement of the maximum clique in the conflict relationship graph is solved to determine the set of accepted vehicle reservation requests, enabling more vehicle reservation requests to be successfully processed in unit time while ensuring safe driving at the intersection and improving intersection throughput efficiency. Third, based on the maximum clique method, a taboo search method is used to search the neighborhood, thus improving the quality of the final solution with a smaller search cost. Simulation results show that compared to other control strategies, such as the FCFS (First Come First Served) strategy, the traffic signal control strategy (Traffic-Light), and the control strategy based on greedy algorithm search (Batch-Light), the proposed strategy can considerably reduce the average vehicle waiting time by 42%, 19%, and 10%, respectively, as well as increasing the number of vehicles passing through the intersection per unit of time by 35%, 20%, and 12%, respectively. These results demonstrate the effectiveness of the proposed strategy in improving the throughput of the intersection and reducing the average vehicle waiting time. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry)
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15 pages, 790 KiB  
Article
Obstacle Avoidance Control of Unmanned Aerial Vehicle with Motor Loss-of-Effectiveness Fault Based on Improved Artificial Potential Field
by Yibo Zhao, Li-Ying Hao and Zhi-Jie Wu
Sustainability 2023, 15(3), 2368; https://doi.org/10.3390/su15032368 - 28 Jan 2023
Cited by 6 | Viewed by 1105
Abstract
This paper presents an obstacle avoidance control strategy for an underactuated quadrotor unmanned aerial vehicle with motor loss-of-effectiveness fault and disturbance. The control system is divided into two parts: the obstacle avoidance loop and the tracking loop. By introducing the height factor in [...] Read more.
This paper presents an obstacle avoidance control strategy for an underactuated quadrotor unmanned aerial vehicle with motor loss-of-effectiveness fault and disturbance. The control system is divided into two parts: the obstacle avoidance loop and the tracking loop. By introducing the height factor in the artificial potential field function, an improved obstacle avoidance strategy is designed in the obstacle avoidance loop. Compared with the existing literature, the proposed obstacle avoidance strategy can avoid falling into the trap of the local optimum when a UAV encounters obstacles. At the same time, considering the sudden motor loss-of-effectiveness fault of UAV, adaptive technology is used to estimate the fault parameters online to restrain the effects of motor loss-of-effectiveness fault in the tracking loop. The stability of the closed-loop UAV system is guaranteed by stabilizing each of the subsystems through backstepping technology. Simulations are conducted to demonstrate the effectiveness of the designed obstacle avoidance control strategy. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry)
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20 pages, 9419 KiB  
Article
A Steady-State Flight Control Algorithm Combining Stretching Ratio Coefficient and PID Control for UAVs in Uncertain Environments
by Jialiang Wang, Rui Tan and Liuyang Nie
Sustainability 2022, 14(22), 14678; https://doi.org/10.3390/su142214678 - 08 Nov 2022
Cited by 1 | Viewed by 1305
Abstract
Unmanned aerial vehicle (UAV) has the advantages of flexible operation, simple structure, and low price, which has been widely researched. In recent years, the application of small UAVs has become more extensive, and the steady-state control of UAVs has great research prospects and [...] Read more.
Unmanned aerial vehicle (UAV) has the advantages of flexible operation, simple structure, and low price, which has been widely researched. In recent years, the application of small UAVs has become more extensive, and the steady-state control of UAVs has great research prospects and value due to it being the key to better execute flight task. A PID steady-state control algorithm based on color recognition and target detection is designed herein. Firstly, it is necessary to calculate the distance between the coordinates of the center of the UAV screen and the geometric center of the target point. Secondly, a pixel distance correction algorithm based on actual distance is proposed so as to correct pixel distance deviation. Finally, it is necessary to control the speed of the UAV by a PID control algorithm to achieve the goal that the UAV is stable near the geometric center of the target point. In short, this algorithm realizes the functions of real-time video transmission of the UAV, flight data storage, color recognition, and speed control of the UAV based on the PID control algorithm and distance correction. The experimental results demonstrate that the proposed algorithm has good robustness, makes the UAV have better stability, and can be used for the process of target tracking in uncertain environments. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry)
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11 pages, 5237 KiB  
Article
Difficulty Evaluation of Navigation Scenarios for the Development of Ship Remote Operators Training Simulator
by Taemin Hwang and Ik-Hyun Youn
Sustainability 2022, 14(18), 11517; https://doi.org/10.3390/su141811517 - 14 Sep 2022
Cited by 4 | Viewed by 1127
Abstract
The enhancement of navigators’ ability has been promoted by on-scene training; however, considering the safety and repeatability, simulation training (ST) is recommended. Notably, the training of maritime autonomous surface ship (MASS) remote operators has to be performed in a systemic simulated environment. In [...] Read more.
The enhancement of navigators’ ability has been promoted by on-scene training; however, considering the safety and repeatability, simulation training (ST) is recommended. Notably, the training of maritime autonomous surface ship (MASS) remote operators has to be performed in a systemic simulated environment. In various fields, ST has differentiated levels of training scenarios considering the proper training effect and evaluation. Although the accuracy and implementation of a realistic situation have received the most attention in simulated navigation, the objective criteria of difficulty are to be established for systemic training. For this purpose, this study aims to propose difficulty criteria in navigation generation scenarios for the development of training simulator MASS remote operators. Proposed methods generated navigation scenarios with differentiated difficulties, simulated navigation experiments were performed, and the results were analyzed as a validation of the differentiated difficulties. Our findings include the difficulty differentiation method, navigation scenario samples, and simulated navigation experimental results. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry)
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Review

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20 pages, 1108 KiB  
Review
The Vagueness of COLREG versus Collision Avoidance Techniques—A Discussion on the Current State and Future Challenges Concerning the Operation of Autonomous Ships
by Krzysztof Wróbel, Mateusz Gil, Yamin Huang and Ryszard Wawruch
Sustainability 2022, 14(24), 16516; https://doi.org/10.3390/su142416516 - 09 Dec 2022
Cited by 10 | Viewed by 3060
Abstract
With the development of Maritime Autonomous Surface Ships (MASS), considerable research is undertaken to secure their safety. One of the critical aspects of MASS is collision avoidance, and multiple collision avoidance algorithms have been developed. However, due to various reasons, collision avoidance of [...] Read more.
With the development of Maritime Autonomous Surface Ships (MASS), considerable research is undertaken to secure their safety. One of the critical aspects of MASS is collision avoidance, and multiple collision avoidance algorithms have been developed. However, due to various reasons, collision avoidance of autonomous merchant vessels appears to be far from resolved. With this study, we aim to discuss the current state of Collision Avoidance Methods (CAMs) and the challenges lying ahead—from a joint academic and practical point of view. To this end, the key Rules from International Regulations for Preventing Collisions at Sea (COLREG) have been reviewed with a focus on their practical application for MASS. Moreover, the consideration of the COLREG Rules in contemporary collision avoidance algorithms has been reviewed. The ultimate objective is to identify aspects of COLREG requiring additional attention concerning MASS developments in terms of collision avoidance. Our conclusions indicate that although a lot of progress has been achieved recently, the feasibility of CAMs for MASS remains questionable. Reasons for so are the ambiguous character of the regulations, especially COLREG, as well as virtually all existing CAMs being at best only partly COLREG-compliant. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles and Intelligent Control System in Future Maritime Industry)
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