Issue 45 | Uncrewed Systems Technology Aug/Sept 2022 Tidewie USV Tupan | Performance monitoring | Bayonet 350 | UAVs insight | Xponential 2022 | ULPower UL350i and UL350iHPS | Elroy Air Chaparral | Gimbals | Clogworks Dark Matter

20 A rtificial intelligence that learns by trial and error, much like a human toddler, and the virtual twin of a real-world UAV, combine to develop flight control software automatically at unprecedented speed using tiny data sets. That is the capability that Flare Bright is developing in its Machine Learning Digital Twin (MLDT) system, according to CEO Kelvin Hamilton. Born in the mid-1970s in a small village in mid-Wales, Hamilton recalls being impressed by Cold War jets roaring overhead in the low-flying training area there, particularly Vulcan bombers, which constituted his introduction to aviation as a boy. An education in electrical and electronic engineering led him initially into underwater robotics, but these days he runs a company dedicated to making UAVs safer and optimising their performance using MLDT technology. Flare Bright runs sparse flight test data and environmental measurements through its own software. “First, we use the drone’s flight characteristics that we calculate from a few seconds of flight data, which is a big advantage over most AI, which requires huge data sets,” Hamilton says. “Second, we factor in wind and environmental influences. We don’t need complex fluid dynamic models here, because we throw every possible scenario at the situation and, more or less, cover a year’s worth of flight tests in a simulated environment in a few hours.” Key to autonomous flight He emphasises that ML unlocks the potential of modelling and simulation in the development of complex technologies. “ML is fantastic at optimising in a huge solution space, which a human brain simply cannot cope with,” he says. “To understand that space and train drones to fly safely is the key to unmanned and autonomous flight.” Flare Bright uses many different types of ML, their selection depending on the problem to be solved and most being optimisation techniques. “Knowing which types of ML to use, and which data sets to throw at them, is one of our key skills,” Hamilton says. “What we avoid is the need to use lots of computing power and large training data sets. We optimise using the data we create, and use edge computing [laptops and desktops] to ensure our code is hyper-efficient, meaning that it solves problems in as few steps as possible.” The use of digital twins is increasingly common for product development, because it can avoid the costs of building physical prototypes. Their application (in conjunction with ML) to creating flight control software is challenging though, because it has to run very fast to achieve worthwhile savings in time and money Flare Bright’s CEO tells Peter Donaldson how the company solves UAV flight control issues at high speed using a machine learning digital twin Twin turbo August/September 2022 | Uncrewed Systems Technology Flare Bright created a digital twin of this UAV under a Defence And Security Accelerator contract to demonstrate autonomous flight without GPS or remote control (Images courtesy of Flare Bright)