Recent Advances and Trends in Marine Vehicles, Automation and Robotics

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 (20 November 2023) | Viewed by 5955

Special Issue Editors

Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam 13120, Republic of Korea
Interests: robotics; Internet of Things (IoT); wireless sensor networks (WSNs); underwater communication and localization; underwater sensor networks (USNs); AI; deep learning
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Special Issue Information

Dear Colleagues,

In recent years, there has been a significant progress in marine vehicles, robotics and autonomous systems, motivated by the impact of increasingly economical, industrial, scientific, and environmental surface and underwater applications. These promising technologies, including USVs (unmanned surface vehicles), ROVs (remotely operated vehicles), AUVs (autonomous underwater vehicles), underwater gliders and marine animal robots, have become the standard tools due to their applicability in underwater observation, deep-sea exploration, underwater mining, environmental data acquisition, geotechnical surveys, renewable energies, multi-robot coordinated and cooperative missions for mapping, underwater intervention and manipulation, aquaculture and fisheries, aquaculture and fisheries, marine infrastructure installation and monitoring, navigation, surveillance, reconnaissance, security, offshore inspection, and transportation. These innovative technologies have shown their high potential to transform our ways of exploring, intervening, and using the marine environment, from the sea-surface to the sea-bed. The expanding utility of these intelligent vehicles offers several appealing advantages for marine experts in terms of minimal risk, reduced operational cost, high reliability, enhanced efficiency, increased intelligence, decrease in environmental footprint, and enlarged application scope. Marine vehicles such as autonomous underwater vehicles are reliable, cost-efficient, safe alternatives to several manned or remotely controlled vehicles. Due to their effective operation in dynamic, harsh, hostile, and complex underwater environments, specifically without human intervention, innovative approaches must be investigated for autonomy, control, navigation, localization, estimation, perception, and guidance. Particularly, enhancing these vehicles’ autonomy, robustness, endurance, collision avoidance, path planning, data fusion, target detection, task management, and sensing necessitates the use of novel artificial intelligence, machine learning, and deep learning techniques. Moreover, there is a dire need to develop advanced and intelligent mission approaches for single and cooperative control of multiple vehicles to undertake more complex operations. In this Special Issue, innovative and frontier research to address aforementioned aspects is included.

This Special Issue aims to highlight ongoing research activities on the advancement of underwater vehicles or robotics that can be applied in practical operations and enhance the automation level of marine vehicles. We will provide a forum to bring together latest research innovations from both practitioners and leading researchers from diversified interests to unlock the potentials and address breakthrough novelties of marine vehicles and robotics. Our focus is to seek high quality original-contributions, technical papers reporting on potential uncertainties related to control and autonomy marine vehicles, reviews and surveys dealing with the latest developments, ongoing research activities, recent breakthroughs, theoretical works, cutting-edge approaches for experimental validations, real-time missions, and applications and challenges of marine vehicles and robotics. Novel and unique techniques, as well as advances in existing techniques, that focus on the future of marine vehicles and robotics are invited for peer review publication. We encourage engineering and science articles on instrumentation or data analysis of marine vehicles contributing to a better understanding of the marine environment. We wish to present a comprehensive overview of the state of the art in the modeling, control, navigation, cooperation, guidance, state estimation and localization of marine vehicles and robotics. Submissions on simulations, real-time sea trials, and testbed applications are strongly considered in this Special Issue. Potential topics of interest for publication include, but are not limited to, the following:

  • New challenges and trends in marine robotics;
  • Innovative underwater robotics for ocean observance;
  • Advanced technologies for maritime archaeology;
  • Marine vehicles and robotics for smart maritime transportation;
  • Marine vehicles for intelligent, smart, and safe marine navigation;
  • Maritime autonomous surface ships;
  • Application of marine vehicles in maritime safety;
  • Autonomy and control of marine vehicles;
  • Applications of marine vehicles in maritime environments;
  • Underwater communications for autonomous underwater vehicles (AUVs);
  • Motion control, localization, navigation, and path planning of marine vehicles;
  • Advances in autonomous underwater robotics based on machine learning;
  • Novel techniques and equipment for underwater robots;
  • Autonomous marine vehicle operations;
  • Advances in underwater robots for intervention;
  • Marine vehicles for maritime internet of things;
  • Design, testing, and operation of marine vehicles.

Dr. Syed Agha Hassnain Mohsan
Dr. Inam Ullah
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

  • marine autonomous system
  • ocean monitoring
  • communication
  • marine vehicles
  • automation operations
  • underwater robotics
  • navigation and guidance
  • motion planning

Published Papers (4 papers)

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Research

12 pages, 1820 KiB  
Article
Testing Galileo High-Accuracy Service (HAS) in Marine Operations
by Pedro Pintor, Manuel Lopez-Martinez, Emilio Gonzalez, Jan Safar and Ronan Boyle
J. Mar. Sci. Eng. 2023, 11(12), 2375; https://doi.org/10.3390/jmse11122375 - 16 Dec 2023
Viewed by 1518
Abstract
Global Navigation Satellite System (GNSS) technology supports all phases of maritime navigation and serves as an integral component of the Automatic Identification System (AIS) and, by extension, Vessel Traffic Service (VTS) systems. However, the accuracy of standalone GNSS is often insufficient for specific [...] Read more.
Global Navigation Satellite System (GNSS) technology supports all phases of maritime navigation and serves as an integral component of the Automatic Identification System (AIS) and, by extension, Vessel Traffic Service (VTS) systems. However, the accuracy of standalone GNSS is often insufficient for specific operations. To address this limitation, various regional and local-area solutions have been developed, such as Differential GNSS (DGNSS), Satellite Based Augmentation Service (SBAS) and Real Time Kinematic (RTK) techniques. A notable development in this field is the recent introduction of the Galileo High-Accuracy Service (HAS), which saw its initial service declared operational by the European Commission (EC) on 24 January 2023. Galileo HAS provides high-accuracy Precise Point Positioning (PPP) corrections (orbits, clocks and signal biases) for Galileo and GPS, enhancing real-time positioning performance at no additional cost to users. This article presents the results of the first Galileo HAS testing campaign conducted at sea using a buoy-laying vessel temporarily equipped with a Galileo HAS User Terminal. The results presented in this Article include accuracy and position availability performance achieved using the Galileo HAS User Terminal. The article also highlights challenges posed by high-power radio-frequency interference, which likely originated from the Long-Range Identification and Tracking (LRIT) system antenna on board the vessel. Furthermore, the article provides additional assessments for different phases of navigation, demonstrating better performance in slow-motion scenarios, particularly relevant to mooring and pilotage applications. In these scenarios, values for horizontal accuracy reached 0.22 m 95% and 0.13 m 68% after removing interference periods. These results are in line with the expectations outlined in the Galileo HAS Service Definition Document (SDD). Full article
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20 pages, 10770 KiB  
Article
Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication
by Habib Hussain Zuberi, Songzuo Liu, Muhammad Bilal, Ayman Alharbi, Amar Jaffar, Syed Agha Hussnain Mohsan, Abdulaziz Miyajan and Mohsin Abrar Khan
J. Mar. Sci. Eng. 2023, 11(11), 2184; https://doi.org/10.3390/jmse11112184 - 17 Nov 2023
Viewed by 1106
Abstract
The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal [...] Read more.
The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders. Full article
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24 pages, 19680 KiB  
Article
Autonomous Visual Fish Pen Inspections for Estimating the State of Biofouling Buildup Using ROV
by Matej Fabijanić, Nadir Kapetanović and Nikola Mišković
J. Mar. Sci. Eng. 2023, 11(10), 1873; https://doi.org/10.3390/jmse11101873 - 26 Sep 2023
Cited by 1 | Viewed by 779
Abstract
The process of fish cage inspections, which is a necessary maintenance task at any fish farm, be it small-scale or industrial, is a task that has the potential to be fully automated. Replacing trained divers who perform regular inspections with autonomous marine vehicles [...] Read more.
The process of fish cage inspections, which is a necessary maintenance task at any fish farm, be it small-scale or industrial, is a task that has the potential to be fully automated. Replacing trained divers who perform regular inspections with autonomous marine vehicles would lower the costs of manpower and remove the risks associated with humans performing underwater inspections. Achieving such a level of autonomy implies developing an image processing algorithm that is capable of estimating the state of biofouling buildup. The aim of this work is to propose a complete solution for automating the said inspection process; from developing an autonomous control algorithm for an ROV, to automatically segmenting images of fish cages, and accurately estimating the state of biofouling. The first part is achieved by modifying a commercially available ROV with an acoustic SBL positioning system and developing a closed-loop control system. The second part is realized by implementing a proposed biofouling estimation framework, which relies on AI to perform image segmentation, and by processing images using established computer vision methods to obtain a rough estimate of the distance of the ROV from the fish cage. This also involved developing a labeling tool in order to create a dataset of images for the neural network performing the semantic segmentation to be trained on. The experimental results show the viability of using an ROV fitted with an acoustic transponder for autonomous missions, and demonstrate the biofouling estimation framework’s ability to provide accurate assessments, alongside satisfactory distance estimation capabilities. In conclusion, the achieved biofouling estimation accuracy showcases clear potential for use in the aquaculture industry. Full article
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16 pages, 5051 KiB  
Article
CR-NBEER: Cooperative-Relay Neighboring-Based Energy Efficient Routing Protocol for Marine Underwater Sensor Networks
by Altaf Hussain, Tariq Hussain, Inam Ullah, Bahodir Muminov, Muhammad Zubair Khan, Osama Alfarraj and Amr Gafar
J. Mar. Sci. Eng. 2023, 11(7), 1474; https://doi.org/10.3390/jmse11071474 - 24 Jul 2023
Cited by 3 | Viewed by 1256
Abstract
This paper proposes a Cooperative-Relay Neighboring-Based Energy-Efficient Routing (CR-NBEER) protocol with advanced relay optimization for MUSN. The utilization of the relay nodes, among all other sensor nodes, makes it possible to achieve node-to-node deployment. The proposed method focuses only on cooperation and relay [...] Read more.
This paper proposes a Cooperative-Relay Neighboring-Based Energy-Efficient Routing (CR-NBEER) protocol with advanced relay optimization for MUSN. The utilization of the relay nodes, among all other sensor nodes, makes it possible to achieve node-to-node deployment. The proposed method focuses only on cooperation and relay optimization schemes. Both schemes have previously been implemented, and thus the proposed method represents the extended version of the Neighboring-Based Energy-Efficient Routing (NBEER) protocol. Path loss, end-to-end delay, packet delivery ratio, and energy consumption parameters were considered as part of the performance evaluation. The average performance was revealed based on simulations, where the overall average EED of Co-UWSN was measured to be 35.5 ms, CEER was measured to be 26.7 ms, NBEER was measured to be 27.6 ms, and CR-NBEER was measured to be 19.3 ms. Similarly, the overall EC of Co-UWSN was measured to be 10.759 j, CEER was measured to be 8.694 j, NBEER was measured to be 8.309 j, and CR-NBEER was measured to be 7.644 j. The overall average PDR of Co-UWSN was calculated to be 79.227%, CEER was calculated to be 66.73.464%, NBEER was calculated to be 85.82%, and CR-NBEER was calculated to be 94.831%. The overall average PL of Co-UWSN was calculated at 137.5 dB, CEER was calculated at 230 dB, NBEER was calculated at 173.8 dB, and CR-NBEER was calculated at 79.9 dB. Based on the simulations and evaluations, it was observed that the cooperation and relay optimization scheme outperformed previous schemes. Full article
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