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

38 M achine learning (ML) and AI are used in many different ways and in different parts of uncrewed systems for a range of functions to boost their performance. For example, AI is used extensively for applications based on image processing. These vary tremendously, from providing an alternative to airborne navigation to monitoring infrastructure. AI can be considered the overarching term for ML for decision-making, particularly with the ability to learn, while ML itself is the ability of systems to make decisions on data without specific instructions. At the heart of this is the neural network, which is inspired by biological networks that have layers of interconnected nodes, or neurons. Each neuron in one layer is connected to every neuron in the next layer via a filter that uses a weighting system to determine the probability of a connection to a node in the next layer. Networks with several layers are called deep neural networks (DNNs), and for image processing the most common type is the convolutional neural network (CNN). There are other types of network, for natural language processing for voice control or signal chain processing for predictive maintenance (as detailed in UST 45, August/September 2022). Neuromorphic networks, also known as spiking neural networks, are also increasingly popular for low-power systems such as UAVs (see sidebar) that need image recognition, as these are only active when there is a change in the signal. Nick Flaherty examines the issues around training neural networks in uncrewed systems Neural implants December/January 2023 | Uncrewed Systems Technology Capturing individual elements in an image using machine learning for autonomous driving (Courtesy of Mobileye)