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

23 we can strip out all the nonsensical results that show paths that the aircraft will just never fly. The navigation error grows all the time – you can’t do anything about that – but we manage to keep the growth linear, so it gives us a lot longer before we lose too much accuracy to the drift.” He adds that knowing the maximum distance the aircraft could be from the position the navigation system calculates is very useful for anti-spoofing, for example. “If you say, ‘OK, we’ve flown for 5 minutes, the maximum we can be from our point is 10 m let’s say, but our GPS is showing that we are 50 m away from where it thinks we should be’, there has either to be an error in a sensor or you are being spoofed,” he says. “In defence scenarios, and increasingly in civil ones, that is going to become a big issue.” Flare Bright’s work in this area has attracted funding from the US DoD and the UK MoD. Complementing VSLAM This ability to constrain navigation errors also makes MLDT a useful complement to other GNSS-independent techniques, such as Visual Simultaneous Localisation and Mapping (VSLAM). “I have spent many years working with VSLAM, and the main problem is making them robust with the processor capability available,” Hamilton says. “They can also be quite flaky in challenging situations. Our system could kick in when VSLAM is struggling to find ground truth. “We look at it as a full stack of navigation sources. If you have an average drone, you start off flying on GNSS, and that gives you a reasonable degree of accuracy. Then, typically, if you need more accuracy or are worried about GNSS interference, outages, jamming and so on, you need an extra system, and the first one you add is likely to be some sort of VSLAM. “Generally, they are tried and tested, and work well in certain scenarios and conditions, but they become flaky and fail in others. If you’re in fog or a snowstorm, or flying over a lake or a desert, you’ve got no reference points so you can’t rely on VSLAM. “As the next layer in the stack, we will kick in and give you that extra 1 to 5 minutes of accuracy you need while your VSLAM system is trying to pick up reference points and understand where it is again. “We’re working on a product now that combines all those systems into one, with ML being used to boost overall performance.” Hamilton argues that MLDT could make an important contribution to the certification of UAVs for operation in national airspace. “We are about to begin a UK government future flight programme where we are focusing on demonstrating how our models can assure the safety of other drones by accurately predicting what they are going to do in every scenario,” he says. “That could be part of the safety case a regulator would use.” Uncrewed Systems Technology | August/September 2022 Now in his mid-40s, Kelvin Hamilton enjoyed computer science and physics at school, but left at 15 – “It just wasn’t for me,” he says – to start work in an electronics factory, from which he did day- release courses to earn ONC and HNC qualifications in electrical and electronic engineering, working his way up to becoming an r&d technician. A degree in those subjects from Cardiff University followed, leading to a PhD in autonomous systems pursued in the Ocean Systems Lab at Heriot-Watt University, in Edinburgh. Here, lab leader Professor David Lane supervised his PhD and became a mentor. “He gave me the chance to get into autonomous systems and provided lots of good advice, as well as a great team and facilities,” he says. In parallel with that, he developed his own simulated underwater exploration robotics project, teaching himself to program in C++ along the way and winning a small contract to build a subsea robot. He was attracted to autonomous systems by the idea of robots’ ability to explore where humans can’t go. “My initial love was space exploration, but I realised it’s very hard to develop something and get it fielded, which is why I went into subsea,” he says. A 20-year career followed, in which he worked for variable-speed drive developer Control Techniques and co-founded subsea autonomy specialist SeeByte. He started Flare Bright in November 2015 as a means to explore ideas and their feasibility, preparing to grow as soon as a clear business case became apparent. “That happened in late 2017, when our CTO Conrad Rider joined, followed by our CCO Chris Daniels in April 2019,” he says. These days, 14 people report to Hamilton, albeit indirectly, and he reports that the company is growing steadily. “Most of the company’s staff are engineers, and mostly ML software engineers trained in-house, who originally started as mathematicians and physicists,” he says. “It’s difficult to find the exact skill set so we take on people with ‘the spark’ and let them loose on what interests them while training them up.” Kelvin Hamilton