Uncrewed Systems Technology 047 l Aergility ATLIS l AI focus l Clevon 1 UGV l Geospatial insight l Intergeo 2022 report l AUSA 2022 report I Infinity fuel cell l BeeX A.IKANBILIS l Propellers focus I Phoenix Wings Orca

40 Focus | AI send these back to a central server to provide more training data. These tools already run on millions of vehicles using ADAS collision detection systems, building up more data for training autonomous systems. There is also a safety aspect. For a driverless car, for example, the framework is part of the safety case, so it is certified in a particular form. If that changes with more training then extra safety certification can be required. There is also an issue called sparsity. Many of the images collected have large areas that are not relevant but still have associated weights. That creates huge frameworks of gigabytes of data where most of the data is irrelevant. Being able to filter out that irrelevant data during training is a key way to make the frameworks smaller and more efficient, but it can require more complex training hardware. Wind turbine inspection The task of inspecting and maintaining wind turbines, both onshore and offshore, is becoming increasingly challenging, and there is a clear need for faster, safer inspections that produce high-quality data in order to pursue preventative maintenance and reduce the requirement for technicians to attend turbines. ML techniques have been used specifically to inspect wind turbines. The basic principle is the complete automation of the inspection process. In one application a tablet device allows an operator to command a UAV to take off to collect high- quality data of the whole turbine in less than 20 minutes, autonomously. After landing, a cloud-based system uses an ML framework to process the images to detect any damage. That allows engineers to view the results of an inspection within 48 hours rather than the traditional time of around 2 weeks. The input data is collected using Lidar and a camera as well as an inertial measurement unit (IMU). The data is compared against a simplified 3D model of the structure being inspected, allowing the location of the asset to be identified and providing the optimal positioning of the system. The ML framework is built in PyTorch, which is open source and helps develop rapid prototypes and ultimately take the 3D models through to production, deploying object detection models. Implementing processes such as data augmentation, model tracking, performance monitoring and model retraining is easy to navigate. The framework is trained and retrained locally using a dedicated ML workstation, which uses commercial RTX graphics cards using a mainstream GPU. The main limitation is the GPU’s memory, with memory requirements being a challenge owing to the size of the raw data at 45 MP. Research has shown this combination of UAVs and AI can be 14% more accurate in detecting faults in wind turbines when compared with human experts carrying out the same inspections. Using AI also reduces the data review time by 27% to achieve that 2-day turnaround. The framework is constantly being updated to improve the performance of the model and the accuracy of the results. For example, the model is being enhanced to make accurate predictions about blade damage and make generalisations about new situations and new types of images. The AI can be extended to other types of infrastructure, such as solar farms. The positioning system would be combined with an ML framework trained on images of solar panels to identify cracks in the panels and supports. December/January 2023 | Uncrewed Systems Technology Simulating an environment to train a UAV with the AirSim AI framework (Courtesy of Microsoft) An AI unit being added to a DJI UAV for wind turbine monitoring (Courtesy of Perceptual Robotics)