Rovco are developing a system that will improve the way in which subsea assets are managed through the development of a 3D modelling process which will allow inspection personnel to be based onshore. The aim of the system is to make asset management far more cost efficient, while also improving safety for staff and the environment. The final product will allow chartered offshore vessels on inspection campaigns to be smaller, while reducing costs and the number of personnel required at sea. Using visual 3D models of subsea assets will allow onshore assessment by all interested parties meaning decision making can be referred as needed, and onshore communication between all of the parties will be made more effective. This will allow faults to be spotted more efficiently and repairs to be made to damaged assets well before the point of failure, decreasing the chance of environmental pollution by mitigating the chance of corrosion going undetected. Initially a feasibility study will be conducted to ensure that industry is ready for this solution, and that it is viable. Alongside this an example of the final output will be produced to help recruit collaborators and assist in defining the project direction during market analysis.

The A2I2 project have been developing untethered, underwater robotic platforms which make unmanned operations the standard for inspection and light intervention tasks for physical infrastructure, primarily in the offshore and nuclear domains. We are developing small, hover-capable Autonomous Underwater Vehicles (AUVs) equipped with a novel visual mapping system and enhanced & verified on-board autonomy. The designs of the platforms are scalable enabling work across domains. They are capable of conducting very close inspection and intervention tasks, such as cathodic protection surveys, coring, visual inspection and metrology and moving small items (e.g. in nuclear ponds). The primary sensing apparatus is Rovco's SubSLAM stereo camera system, with associated computer vision software to allow the creation of 3D maps of the environment. The 3D data serves multiple purposes: enabling safe navigation in complex, cluttered environments by providing a 3D occupancy grid for guidance software; allowing localisation of targets detected by machine learning object recognition; and as metric survey output. To enable the A2I2 robots to manoeuvre safely near sensitive infrastructure, the consortia are addressing current technological limitations: * Perception in cluttered underwater environments * Robust (Fail Safe) operation near sensitive infrastructure * Precision manoeuvring and control near infrastructure * Communication with robots in challenging underwater environments. A2I2 provides a step-change beyond SotA in artificial intelligence control and communications. The consortia members working in the offshore domain (Rovco, NOC and D-RisQ), supported by the Offshore Renewable Energy Catapult, will use this additional funding to demonstrate, verify and validate the system in the offshore operational environment. This will see improvements made to the perception system and the vehicle and would incorporate last response engine software to manage behaviour in the event of connection loss or other critical events. We will develop a safety case for the system and the project will culminate by demonstrating a semi-autonomous survey of an asset (i.e export cable and monopile) with a human in the loop. This is a significant step-change in operational capability. The system will ultimately remove the need for pilot operators to be present on the vessel, as the vehicle requires only supervision which can be conducted remotely. This in turn reduces the costs and environmental impact of operations by allowing smaller, less fuel consuming vessels to be used. The current drive to reduce crew as a direct impact of COVID-19 is accelerating the adoption of remote and autonomous technologies, this follow-on project performs the V&V demonstrations to accelerate the technology to market.

"Safe and efficient construction, operation and decommissioning of subsea assets is critically important to UK and worldwide energy production. This is particularly true for offshore renewable energy where cost efficiencies are necessary to deliver clean power that is cost competitive with other low carbon systems and at an affordable scale. From construction to decommissioning, underwater survey provides the data to monitor condition, predict asset life and ensure the environment is protected. We aim to deliver a step change in efficiency and safety by delivering live, dense, 3D point cloud data from small, Remotely Operated Underwater Vehicles. This will enable smaller vessels to be used with fewer crew, no divers, and removing the need to put people at risk. Compared to traditional visual survey, 3D data allows accurate measurement and repeatable, reliable metrics for asset condition monitoring and automatic monitoring from autonomous underwater vehicles (AUVs). Ultimately, live 3D enables accurate navigation for fully autonomous inspection AUVs reducing manpower and increasing efficiency yet further. Currently, AUVs do not possess the detailed mapping and localisation required for visual inspection work. Quality 3D visual data is also a prerequisite to applying artificial intelligence and deep learning solutions to 3D images thereby enabling greater autonomy and reliably repeatable measurements. AUV3D Phase 2 continues from the successful phase-1 project, which saw Rovco develop and demonstrate technical feasibility of live underwater 3D reconstruction from vision. This took place in the Offshore Renewable Energy Catapult's Blyth test facilities, where a dry dock with test targets was used to test and evaluate the system. For phase-2, the goal is to extend and improve on this both in terms of the underpinning technology and with more representative testing both in test tank and at sea. The prototype developed in Phase-1 enables innovative real-time underwater 3D survey from video, and for phase-2 we extend this into a more complete solution, considering integration with additional sensors and the delivery of live survey data to shore. By demonstrating the software and hardware necessary to produce live 3D data from cameras in the challenging and extreme subsea environment we enable the development of a complete vision based underwater Robotic Artificial Intelligence (RAI) survey solution. This is vital to create small, capable, intelligent autonomous vehicles and allow more efficient survey with fewer people in harm's way."

Safe and efficient construction, operation and decommissioning of subsea assets is critically important to UK and worldwide energy production. This is particularly true for offshore renewable energy where cost efficiencies are necessary to deliver clean power power that is cost competitive with other low carbon systems and at an affordable scale. From construction to decommissioning, underwater survey provides the data to monitor condition, predict asset life and ensure the environment is protected. We aim to deliver a step change in efficiency and safety by delivering live, dense, 3D point cloud data from small, Remotely Operated Underwater Vehicles. This will enable smaller vessels to be used with fewer crew, no divers, and removing the need to put people at risk. Compared to traditional visual survey, 3D data allows accurate measurement and repeatable, reliable metrics for asset condition monitoring. Ultimately, live 3D enables accurate navigation for fully autonomous underwater vehicles reducing manpower and increasing efficiency yet further. Quality 3D visual data is also a prerequisite to applying artificial intelligence and deep learning solutions to 3D images thereby enabling greater autonomy and reliably repeatable measurements. The key objective of the AUV3D project is to prototype and demonstrate the feasibility of a high-quality underwater, intelligent, stereo camera system. This system will enable innovative, real-time processing of underwater 3D from ROV video survey. To do this we will exploit recent advances in both camera technology and embedded GPU computing, and together these technologies enable Artificial Intelligence to be used to accurately to assess underwater 3D scenes. By demonstrating the feasibility of the software and hardware necessary to produce live 3D data from cameras in the challenging and extreme subsea environment we enable the development of a complete vision based underwater Robotic Artificial Intelligence (RAI) survey solution. This has the potential to create small, capable, intelligent autonomous vehicles and allow more efficient survey with fewer people in harm's way.

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