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Platformone A group of researchers has developed neural network algorithms that can work on nano-UAVs weighing as little as 10 g (writes Nick Flaherty). The nano-UAVs are increasingly useful for monitoring applications, but struggle with complex autonomous operations with multiple objectives. Researchers from the University of Bologna, Italy, KU Leuven in Belgium, the Dalle Molle Institute for Artificial Intelligence, USI- SUPSI, in Switzerland and the Integrated Systems Laboratory at ETH Zurich, Switzerland, successfully used time-of-flight distance sensor data with a vision-based convolutional neural network (CNN) for object detection in tests of the autonomous systemwith multiple objectives. The test UAV is a Bitcraze Crazyflie 2.1 quadrotor, a COTS UAV that weighs 27 g and has a diameter of 10 cm. It is equipped with two microcontroller units (MCUs), a single-core ARM Cortex-M4 (STM32) for safe navigation and exploration policies, and a parallel ultra-low power eight-core GAP8 that uses the RISC-V instruction set. The GAP8 has been optimised for CNN inference, with a power envelope of only 134 mW, including the image sensors and external memories. The challenge of providing autonomous operation for the UAV is addressed by mapping multiple tasks on the two MCUs. The STM32F405 MCU, with a peak performance of 100 million MAC operations per second, is used for the lightweight workloads such as sensor interfaces. The GAP8 MCU is used for the machine learning and image detection algorithms. TheUAV has three additional PCBs: the Flowdeck, theMulti- ranger deck and theAI deck. The Flowdeck provides optical flow and heightmeasurements to increase state estimation reliability. The Multi-ranger deck features five single-beam VL53L1x time- of-flight distance sensors mounted on the UAV’s top and sides, providing line-of-sight distance measurements within 40 cm. The AI deck is a visual engine running on the GAP8 chip at 175 MHz, with a low-power QVGA-resolution grayscale camera and additional off-chip memories, including 8 Mbytes of HyperRAM and a 64 Mbytes of HyperFlash memory. The UAVwas testedwith various algorithms, and flew around a roomwithmultiple objectives. The best results achieved a final detection rate of 90%, exploiting a pseudo-randompolicy for explorationwith the largest object detectionmodel and amean flight speed of 0.5m/s. The higher detection rate can be reached by trading off the CNN’s detection capabilities with the flight speed. Airborne vehicles Neural net nano-UAVs Dr DonoughWilson Dr Wilson is innovation lead at aviation, defence, and homeland security innovation consultants, VIVID/futureVision. His defence innovations include the cockpit vision system that protects military aircrew from asymmetric high-energy laser attack. He was first to propose the automatic tracking and satellite download of airliner black box and cockpit voice recorder data in the event of an airliner’s unplanned excursion from its assigned flight level or track. For his ‘outstanding and practical contribution to the safer operation of aircraft’ he was awarded The Sir James Martin Award 2018/19, by the Honourable Company of Air Pilots. Paul Weighell Paul has been involved with electronics, computer design and programming since 1966. He has worked in the real-time and failsafe data acquisition and automation industry using mainframes, minis, micros and cloud- based hardware on applications as diverse as defence, Siberian gas pipeline control, UK nuclear power, robotics, the Thames Barrier, Formula One and automated financial trading systems. IanWilliams-Wynn Ian has been involved with uncrewed and autonomous systems for more than 20 years. He started his career in the military, working with early prototype uncrewed systems and exploiting imagery from a range of systems from global suppliers. He has also been involved in ground-breaking research including novel power and propulsion systems, sensor technologies, communications, avionics and physical platforms. His experience covers a broad spectrum of domains from space, air, maritime and ground, and in both defence and civil applications including, more recently, connected autonomous cars. Professor James Scanlan Professor Scanlan is the director of the Strategic Research Centre in Autonomous Systems at the University of Southampton, in the UK. He also co-directs the Rolls-Royce University Technical Centre in design at Southampton. He has an interest in design research, and in particular how complex systems (especially aerospace systems) can be optimised. More recently, he established a group at Southampton that undertakes research into uncrewed aircraft systems. He produced the world’s first ‘printed aircraft’, the SULSA, which was flown by the Royal Navy in the Antarctic in 2016. He also led the team that developed the ULTRA platform, the largest UK commercial UAV, which has flown BVLOS extensively in the UK. He is a qualified full-size aircraft pilot and also has UAV flight qualifications. Uncrewed Systems Technology’s consultants 17 Uncrewed Systems Technology | February/March 2023