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

21 – ideally orders of magnitude faster than real time, Hamilton says. It is here that the ability to write ‘hyper-efficient’ code becomes essential. Back to efficient code He points out that in the early days of personal computers, their relatively meagre processing power and memory made it essential to write efficient code, but the art has faded as computing power and software complexity have grown. “Because software is so complex now, most people don’t write efficient code,” he says. “What they do is write blocks of code, and then if you have to go through that block 10 times in order to achieve the answer it doesn’t matter, because everything runs so quickly. So efficiency is not something that coders have really been taught properly for 20-odd years.” Hamilton points out that the company’s CTO, Conrad Rider, is a former champion at solving Rubik’s Cube, with a particular knack for solving it in the fewest moves. “That same sort of thinking is how he implements the software architecture and code,” Hamilton says. “That is part of the DNA of all our software engineers now, and we assess new recruits on their ability to learn these techniques rapidly. Rider wrote the entire company software ecosystem from scratch to be optimised for ML.” Solving problems in as few steps as possible is directly relevant to writing efficient code that can characterise a UAV using a minimum of real-world data to create its digital twin, then have the computer run through a very large number of scenarios in which it learns by trial and error to reproduce the behaviour of the real aircraft, checked periodically against flight test results. Hamilton emphasises that Flare Bright’s software needs relatively very small amounts of real-world data, which he refers to as “ground truth”, on which to build. He says, “Most AIs need a lot of ground truth, a huge data set of, say, 100,000 pictures of a bridge and 100,000 pictures of a pylon, tasking the computer with looking at them all and working out which are which. It will get better and better at that through reinforcement learning.” Tiny data sets “Our data sets when we were developing our aircraft were six one-second flights,” he says. “That’s all the data we need because it gives us a good enough approximation for our model to throw every scenario we can imagine at it, and because it works so quickly, we can do that overnight. “We can run hundreds of thousands of scenarios with different wind vectors, angles of flying through the wind, strengths, gusts and so on. From the sensor feeds on the aircraft we’ll know enough in those six one-second flights to know roughly how it flies. Then the digital twin tunes and optimises the flight control program by getting it closer and closer to the way it thinks the aircraft should fly. “Normally we get it pretty much right – over 90% – first time. It’s not always perfect, but then we can use the process again. That means we are saving about 90% of costs and time on any standard flight test programme.” Accelerated development Hamilton says MLDT can accelerate development times by a factor of 10, and also produces some remarkable capabilities almost as a by-product. “For example, using our MLDT we can sense the wind in flight without using pitot tubes or any other direct measurement systems,” he says. “In turn, that allows the crucial wind modelling needed for flight path planning in complex urban areas to be verified by every drone that flies, which in turn allows optimal paths to be flown. The paths themselves can be optimised using MLDT, taking into account not just the wind around buildings but accurate capabilities of each drone.” This path optimisation process can yield some counter-intuitive results, he adds, citing a flight profile that the computer was optimising for maximum time spent observing a selected target before returning the UAV to the launch point. “What the MLDT gave us resembled a handbrake turn at apogee,” he says. “Our initial inclination was to Kelvin Hamilton | In conversation Uncrewed Systems Technology | August/September 2022 Through multiple flight iterations, the Machine Learning Digital Twin system closes the gap between the predicted and real-world flight paths of the virtual and real UAVs