Visual inspection of large infrastructure assets in extreme environments, such as offshore wind turbines, is essential for maintaining performance and ensuring safety. However, it is expensive, labour intensive, and hazardous, with the majority of inspections being carried out manually by inspectors using rope access or boom lift. Currently tens of millions of man hours per annum are spent looking for defects in safety critical infrastructure. Automatic visual inspections using robots equipped with cameras offers a potential solution, significantly reducing costs, increasing quality assurance and reducing safety concerns. Major progress has been made in capturing images for inspection, notably using drones, and in some cases, these have advanced to the point of being technically viable alternatives to traditional manual inspections. However, although the capture of images has been automated, the processing of images in order to identify defects remains a semi-automated process, with reliance on visual inspections of image data by trained experts. This means that although the visual data can be collected automatically and quickly, inspections still take a significant amount of time. Hence there is a need for fully automating the data processing component if these systems are to fully realise their potential. Alongside this there is also a need to garner wider acceptance of automatic processing techniques across the industry. They need to be commercially viable and demonstrably reliable - adoption of new approaches to inspection relies on a full understanding of their limitations and the criteria by which they can be assessed and categorised. This is necessary if such methods are to be acceptable to current and future regulatory requirements. Addressing this issue is therefore of equal importance to technical development.This project aims to address both of these issues. It is a collaboration between Garrad Hassan (DNV-GL), world leaders in risk management and quality assurance, Perceptual Robotics (PR), an SME specialising in visual inspection of wind turbines using drones, and the University of Bristol (UoB), experts in computer vision and AI. An automated processing pipeline will be developed and demonstrated, and incorporated within PRs Dhalian system, a world leading semi-automated drone inspection system for wind turbines. Alongside this, a general framework for assessing, characterising and comparing such systems will be validated and verified, with the aim of generating broader acceptance across the industry and informing future regulation. The project will provide both competitive advantage to PR and contribute to growth of the UK automated inspection industry.

"**Project vision** The Multi-Platform Inspection, Maintenance & Repair in extreme environments (MIMRee) project will introduce a step-change in the Operations and Maintenance (O&M) of offshore wind farms by removing humans from the loop during the inspection, maintenance and repair (IMR) of offshore wind turbine blades. The aim is to significantly reduce the costs and turbine downtime associated with these tasks and reduce the H&S risks of using rope access technicians. In this project, the multi autonomous platform approach will be demonstrated for a use case in offshore renewables; however, the developed autonomous vehicle surface vessel hub, Human-Machine Interface (HMI), robotic teaming and communications, and automated mission planning will also have applications in the offshore Oil & Gas, Search and Rescue and Defense sectors. **Key objectives** * Remove the need to send humans offshore to carry out wind turbine blade IMR tasks; * Remove the need to shut wind turbines down to carry out blade inspections; * Reduce the risk of using autonomous vehicles offshore to carry out asset IMR tasks; * Safely demonstrate a fully autonomous approach to blade IMR tasks in real-world operating conditions; * Establish the business case for using autonomous vehicles for blade IMR; * Develop a roadmap for transferring the MIMRee system to other relevant industries. **Main areas of focus** The developed MIMRee system will comprise of an Autonomous Surface Vessel (ASV) with capabilities to autonomously transport, charge and deploy UAVs and blade IMR robots at offshore wind farms. The UAVs will be developed to both autonomously inspect wind turbine blades and deploy blade IMR robots on stationary wind turbine blades. The blade IMR robot will be developed to conduct both autonomous NDT inspections and maintenance and repairs of wind turbine blades. Two-way communication with the ASV and on-board autonomous vehicles will be enabled by a HMI, enabling an onshore operator to both view gathered inspection data and issue automatically generated IMR mission plans. A novel sensor will be developed which can record images of moving wind turbine blades, which could be integrated with the UAVs and/or ASV. All technologies will be tested, validated and demonstrated in representative real-world conditions. **Project team** The project is led by Plant Integrity Limited who are collaborating with Thales UK Limited, Wootzano Limited, BladeBug Limited, Offshore Renewable Energy Catapult, University of Manchester, University of Bristol, Royal Holloway University of London and Royal College of Art to develop and demonstrate a prototype version of the MIMRee."

"Offshore wind is a key energy source for the UK. It will play an increasingly significant role in future years, as part of an energy mix that is moving towards cleaner and more renewable sources. Offshore Wind Turbines (OWTs) have significant environmental challenges in terms of both the marine environment and the weather. This project, led by Perceptual Robotics and in partnership with ASV, the University of Bristol and VulcanUAV - will be developing and testing key technologies to address the autonomous inspection of offshore turbines. Building on an existing capability for the inspection of onshore wind turbines, the team will be working on integrating this with an autonomous surface boat provided by ASV, creating a system which will automatically deploy and recover the inspection drone without the need for human interaction. The long term vision of this project is to enable fully autonomous inspection for OWT - working from an autonomous boat whilst being monitored remotely from land. Key challenges associated with this project include mechanical deployment, robust operations, multi vehicle cooperation, communications and the handling and processing of large datasets. The team consists of specialists in drone design, construction and operation with Perceptual Robotics and VulcanUAV; specialists in autonomous marine vehicles through ASV; experts in computer vision with Bristol University and the ideal facilities in which to develop and test the system at the ORE Catapult facilities. Working together to solve the problems associated with operating an autonomous system in the extreme environment found offshore, the team will need to use modern control theory, sensors, materials, computer technology and AI algorithms to create a platform which can carry out rapid, robust inspections in the marine environment. A fully autonomous system for offshore turbine inspection will not only significantly reduce the costs associated with ongoing inspection, but will also improve the quality and quantity of the inspection data. Modern sensing, including the vision processing offered by the University of Bristol will allow Perceptual Robotics to fly closer and more accurately with respect to the blades, thereby improving the images and maximising the flight envelope. This in turn will offer the potential for accurate condition monitoring and possible lifetime extensions. The UK is currently a world leader in offshore wind energy and this project will provide a further step change in the efficiency and quality of inspections."

Perceptual Robotics is working with the University of Bristol and industry partners to provide fully automated visual inspection of wind turbines using smart autonomous drone technology. One of the major concerns in investing in wind farm projects relates to maintaining turbine availability, which represents the risk of lower energy yields and lost production due to periods of turbine standstill and repair. Maintaining wind turbine reliability is essential for a wind farm to perform effectively and profitably. As a consequence with huge numbers of wind turbines worldwide (315,000+), frequent visual inspection is becoming ever more important. Current techniques using industrial rope access or piloted drones are costly, time-consuming and unable to deliver repeatable and consistent inspection. The aim of this project is to address these weaknesses by developing drone technology which is able to autonomously fulfil the entire inspection to reporting requirement, providing safe, robust, repeatable inspection, reducing costs and increasing trust and quality. Such an approach to inspections will contribute to reducing wind turbine down-time, deliver more affordable operational costs and improve the return on wind farm investment. The technology will include innovative algorithms in flight control and vision based defect detection, and will be developed within a platform independent architecture. It will yield a unique product with significant technology advantage over competitor systems and open up markets in the UK and overseas, further increasing UK expertise in renewable technology.