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

22 In conversation | Kelvin Hamilton reject it because it looked wrong, but it actually worked brilliantly. We just never thought of sacrificing the effectiveness of the control surfaces for a brief instant, even though the aircraft had enough momentum to keep turning. It achieved the objectives better than anything we would have chosen.” Learn like a toddler Hamilton likens the difference in the ways the MLDT and other AIs learn to the difference between the strategies of a toddler and those of a teenager. A teenager will learn by reading books, he says, whereas a toddler learns by experimenting. “The teenage way of learning is the way most neural networks work at the moment,” he says. “They take in lots of data; it’s like doing endless sine and cosine calculations until you’ve trained your brain how to do them. A toddler will try things out, sitting and standing in different ways, falling over and trying something else. They are open to any idea – much to any parent’s horror! “We learn like a toddler. We don’t need to know particular physics and aerodynamics rules; what we need to know is whether it works. As our MLDT progresses, it is getting better and better at understanding what works and what doesn’t.” The system’s judgement about whether a particular combination of control surface movements works or not is based on whatever parameters it has been told to optimise, as measured and fed back by the UAV’s suite of a dozen or so onboard sensors and control surface actuators. “For each data point the sensors provide, we’ll know what the aircraft is doing, and the digital twin will know the ground truth of what’s going on in each of those scenarios,” Hamilton says. “We sample at a very high rate, in the hundreds of hertz, so even in those one-second flights we get enough data from the sensors without needing to use computer capacity in the cloud to run it all. “We’ll test the drone in the real world, and if the AI is giving spurious results, that’s fine, we put in the results from the real world into the machine. It will then run those hundreds of thousands of scenarios again. “The right answer is what happened in the real world – that’s the ground truth – so the AI accepts the results that come closest to that and discards all those that are farthest away, so the machine learns as it goes.” GNSS independence A high-fidelity digital twin of an aircraft, coupled with ML, inherently has some predictive power over its behaviour. Flare Bright has exploited that to constrain the errors in low-cost INSs to provide small UAVs with a useful degree of independence from GNSS. That enables them to tolerate loss of a signal for longer and compensate for spoofing, for example. As Hamilton explains, “Large INSs are established, but are heavy, expensive, consume lots of power and, owing to the laser gyros cannot easily be miniaturised. Lots of academics and start-ups have tried to perfect a chip-based INS, but the navigation error typically runs away exponentially after a few seconds. “We have constrained the error to a linear function using our MLDT techniques, and are achieving a few minutes of accurate inertial navigation in a low-power credit card-sized chip based on a Raspberry Pi CPU.” Constraining the growth in a navigational error from the exponential to the linear is a big deal, so it is worth digging into the idea a little further. “Typically, for every second of flight the error is going to grow a bit more until it runs away exponentially,” Hamilton notes. “In most academic flight control departments, they will manage 10, maybe 15 seconds before the thing is just too inaccurate to be useful. But because we are putting a layer of ML in the digital twin, every time there’s an error we can calculate what the maximum error should be, and then we have it constrained. “So we know in any millisecond how big the error can be and what the compounding effect will be, and therefore August/September 2022 | Uncrewed Systems Technology Flare Bright uses this Snapshot nanodrone as a test bed for its Machine Learning Digital Twin technology to learn flight characteristics in a few flights