Understanding drone sensor data for autonomous flight

Flare Bright is a world-leading aerospace drone and Advanced Air Mobility (AAM) software company developing a Machine Learning (ML) boosted software ecosystem using advanced Digital Twins (DT) that will enable safe, reliable, autonomous Beyond Visual Line Of Sight (BVLOS) flight.

Flare Bright's core ML-DT software ecosystem is leading to two commercially important products, both with huge potential markets and identified as critical enablers to drone technology achieving the economic growth predicted.

Firstly, Flare Bright has developed a high resolution 'sensorless' wind measurement capability that uses the drone itself as the sensor. Other solutions, such as pitot probes, are less accurate, slower in response and not practical from a size and weight perspective. This unique capability has been demonstrated at Cardiff Airport, within UKRI's Future Flight project SafeZone, and the accuracy achieved has been scrutinised in an ICAS 2022 technical paper. The accuracy and resolution of this measurement capability makes it a key enabler in safer flight, allowing more responsive flight control tunes and generating wind data that would enable safe operating limits to be defined in urban or complex flow environments where wind conditions experienced by aircraft are significantly different from averaged forecast data that is currently available.

Secondly, Flare Bright has developed an ML-boosted Inertial Navigation System (INS) that provides accurate short-term navigation when GPS is denied or corrupted using existing, small and inexpensive, on-aircraft sensors. Existing high accuracy INS are large, heavy and consume lots of power, making them unsuitable for drones and electric aircraft of the future, where weight and power are critical. Our capability is therefore key to BVLOS as GPS drop-outs already regularly hinder operation, and, in future, are likely to occur more frequently due to electromagnetic interference from increased technology use and deliberate jamming disrupting signals.

This project aims to address a challenge underpinning both these products. Flare Bright has developed a patent pending technique that enables airflow over the aircraft to be estimated using an embedded DT, from which either wind speed can be estimated if ground velocity is known or vice versa. However, when applied across a wide flight envelope, this technique is hindered by a complex "one-to-many" problem with multiple potential solutions. Using cutting edge mathematical techniques, this project aims to identify if a single solution may be ascertained or calculated to be more likely than the others, thereby significantly increasing the impact and viability of both Flare Bright's core products.