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Operations and Maintenance (O&M) of Offshore Renewal Energy (ORE) Devices...
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Operations and Maintenance (O&M) of Offshore Renewal Energy (ORE) Devices Using Marine Vehicles and Drones

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 11558

Special Issue Editors

Dr. Asiya Khan

E-Mail Website
Guest Editor
Multimedia Communication and Intelligent Control, School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
Interests: prediction and control of video quality using AI, ML, cloud computing, fuzzy logic, applying computer vision techniques, and deep learning in pedestrian recognition; disease identification in cotton crops and damage recognition in wind turbines
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Gianni

E-Mail Website
Guest Editor
Lecturer in Robotics, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: robot planning and control; multirobot coordination and cooperation; machine learning for robotics; robot simulation; robotics in extreme environments
Dr. Sulakshan Rajendran

E-Mail Website
Guest Editor
Research Fellow, Marine-I project, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL8 1JQ, UK
Interests: electric vehicles; unmanned air vehicles; hybrid vehicles/vessels; intelligent control systems; robust control; avionics; AI; autonomous systems

Special Issue Information

Dear Colleagues,

Offshore operations and maintenance (O&M) is a rapidly developing sector with less standardised technical and commercial practices. Based on the UK Government’s projections only, the O&M of ORE devices could be worth almost GBP 2 billion per annum by 2025—an industry similar in size to the UK passenger aircraft service business today. As more ORE projects are built further from shore, accessing the devices to carry out necessary O&M will involve a diverse range of activities. The O&M activity accounts for approximately one quarter of the lifetime cost and contributes to a significant overall cost of energy. Therefore, to reduce the cost of O&M services and optimise asset performance, emerging and disruptive technologies such as edge computing, artificial intelligence (AI), and machine learning (ML) have the potential to address these challenges using IoT and edge devices. These technologies enable data to be processed at the network edge, thus allowing critical data to be collected and processed in real time, reducing the cost and risk to personnel. The International Maritime Organization (IMO) has devised strict rules to reduce the emissions of greenhouse gases (GHG), and the marine sector is expected to shift to greener operation. Therefore, electrification or hybridisation of marine vessels is rapidly increasing, and it is challenging to achieve an optimal performance without losing stability and energy efficiency. Hence, more advanced intelligent integrated navigation and control systems are required to achieve high performance as well as energy efficiency.

This Special Issue aims at collecting high-quality articles including reviews contributing to the advanced techniques using marine vehicles and drones to conduct remote monitoring, environmental monitoring, upkeep, and repair of the offshore physical systems. We welcome submissions from broad topics of interest that include but are not limited to:

  • Artificial intelligence methods for marine vehicles and drones’ control in the marine environment;
  • Robotics and autonomous systems techniques for optimal navigation;
  • Distributed and decentralised control;
  • Hybrid marine vessels and intelligent control;
  • Electric marine propulsion;
  • Edge intelligence for remote monitoring and management;
  • Marine hybrid/swarm systems for data collection and observation;
  • Digital twins and multiscale modeling;
  • Stochastic systems;
  • System identification and modeling;
  • Predictive maintenance and control;
  • Fault detection and diagnosis;
  • Health monitoring;
  • Estimation and filtering;
  • Multiobjective methods and optimisation;
  • Big data systems;
  • Human–machine systems;
  • Other emerging applications of AI, Edge computing, and IoT;
  • Embedded systems.

Dr. Asiya Khan
Dr. Mario Gianni
Dr. Sulakshan Rajendran
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. Drones 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 netcentric systems
  • Offshore operations and maintenance
  • Artificial intelligence
  • IoT and edge computing networking
  • SLAM
  • Modelling and control
  • Digital twinning
  • Hybrid marine vessels
  • Multirobot systems
  • Robotics in extreme environments

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Published Papers (3 papers)

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Research

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30 pages, 9006 KiB  
Article
LiDAR-Based Unmanned Aerial Vehicle Offshore Wind Blade Inspection and Modeling
by Alexandre Oliveira, André Dias, Tiago Santos, Paulo Rodrigues, Alfredo Martins and José Almeida
Drones 2024, 8(11), 617; https://doi.org/10.3390/drones8110617 - 28 Oct 2024
Viewed by 1000
Abstract
The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh [...] Read more.
The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine. Full article
(This article belongs to the Special Issue Operations and Maintenance (O&M) of Offshore Renewal Energy (ORE) Devices Using Marine Vehicles and Drones)
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20 pages, 823 KiB  
Article
A Reinforcement Learning Approach Based on Automatic Policy Amendment for Multi-AUV Task Allocation in Ocean Current
by Cheng Ding and Zhi Zheng
Drones 2022, 6(6), 141; https://doi.org/10.3390/drones6060141 - 7 Jun 2022
Cited by 10 | Viewed by 2460
Abstract
In this paper, the multiple autonomous underwater vehicles (AUVs) task allocation (TA) problem in ocean current environment based on a novel reinforcement learning approach is studied. First, the ocean current environment including direction and intensity is established and a reward function is designed, [...] Read more.
In this paper, the multiple autonomous underwater vehicles (AUVs) task allocation (TA) problem in ocean current environment based on a novel reinforcement learning approach is studied. First, the ocean current environment including direction and intensity is established and a reward function is designed, in which the AUVs are required to consider the ocean current, the task emergency and the energy constraints to find the optimal TA strategy. Then, an automatic policy amendment algorithm (APAA) is proposed to solve the drawback of slow convergence in reinforcement learning (RL). In APAA, the task sequences with higher team cumulative reward (TCR) are recorded to construct task sequence matrix (TSM). After that, the TCR, the subtask reward (SR) and the entropy are used to evaluate TSM to generate amendment probability, which adjusts the action distribution to increase the chances of choosing those more valuable actions. Finally, the simulation results are provided to verify the effectiveness of the proposed approach. The convergence performance of APAA is also better than DDQN, PER and PPO-Clip. Full article
(This article belongs to the Special Issue Operations and Maintenance (O&M) of Offshore Renewal Energy (ORE) Devices Using Marine Vehicles and Drones)
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Review

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26 pages, 1442 KiB  
Review
Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
by Mohd Hisham Nordin, Sanjay Sharma, Asiya Khan, Mario Gianni, Sulakshan Rajendran and Robert Sutton
Drones 2022, 6(6), 137; https://doi.org/10.3390/drones6060137 - 26 May 2022
Cited by 25 | Viewed by 7085
Abstract
Operations and maintenance of Offshore Wind Turbines (OWTs) are challenging, with manual operators constantly exposed to hazardous environments. Due to the high task complexity associated with the OWT, the transition to unmanned solutions remains stagnant. Efforts toward unmanned operations have been observed using [...] Read more.
Operations and maintenance of Offshore Wind Turbines (OWTs) are challenging, with manual operators constantly exposed to hazardous environments. Due to the high task complexity associated with the OWT, the transition to unmanned solutions remains stagnant. Efforts toward unmanned operations have been observed using Unmanned Aerial Vehicles (UAVs) and Unmanned Underwater Vehicles (UUVs) but are limited mostly to visual inspections only. Collaboration strategies between unmanned vehicles have introduced several opportunities that would enable unmanned operations for the OWT maintenance and repair activities. There have been many papers and reviews on collaborative UVs. However, most of the past papers reviewed collaborative UVs for surveillance purposes, search and rescue missions, and agricultural activities. This review aims to present the current capabilities of Unmanned Vehicles (UVs) used in OWT for Inspection, Maintenance, and Repair (IMR) operations. Strategies to implement collaborative UVs for complex tasks and their associated challenges are discussed together with the strategies to solve localization and navigation issues, prolong operation time, and establish effective communication within the OWT IMR operations. This paper also briefly discusses the potential failure modes for collaborative approaches and possible redundancy strategies to manage them. The collaborative strategies discussed herein will be of use to researchers and technology providers in identifying significant gaps that have hindered the implementation of full unmanned systems which have significant impacts towards the net zero strategy. Full article
(This article belongs to the Special Issue Operations and Maintenance (O&M) of Offshore Renewal Energy (ORE) Devices Using Marine Vehicles and Drones)
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