Understanding drone sensor data for autonomous flight

了解无人机传感器数据以实现自主飞行

• 批准号: 10061081
• 金额: $ 1.91万
• 依托单位: FLARE BRIGHT LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10061081
• 关键词: Understanding; drone; sensor; data; autonomous

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.

Flare Bright 是一家世界领先的航空航天无人机和先进空中机动 (AAM) 软件公司，利用先进的数字孪生 (DT) 开发机器学习 (ML) 增强的软件生态系统，该生态系统将实现安全、可靠、自主的超视距 (BVLOS) Flare Bright 的核心 ML-DT 软件生态系统正在开发两款重要的商业产品，这两款产品都具有巨大的潜在市场，并被认为是无人机技术实现预期经济增长的关键推动因素。首先，Flare Bright 开发了高分辨率“无传感器”风测量功能，使用无人机本身作为传感器。其他解决方案，例如皮托管，精度较低，响应速度较慢，并且从尺寸和重量的角度来看并不实用。这种独特的功能已在卡迪夫机场的 UKRI 未来飞行项目 SafeZone 中得到展示，并且所实现的准确性已在 ICAS 2022 技术论文中得到了详细审查。这种测量功能的准确性和分辨率使其成为更安全飞行的关键推动者，允许更灵敏的飞行控制调整并生成风数据，从而能够在城市或复杂的流动环境中定义安全操作限制，在这些环境中，飞机所经历的风况非常严重与目前可用的平均预报数据不同。其次，Flare Bright 开发了一种 ML 增强型惯性导航系统 (INS)，当 GPS 被拒绝或损坏时，使用现有的小型且廉价的机载传感器可提供准确的短期导航。现有的高精度惯性导航系统体积大、重量重且功耗大，不适合重量和功率至关重要的未来无人机和电动飞机。因此，我们的能力对于 BVLOS 至关重要，因为 GPS 丢失已经经常阻碍操作，并且由于技术使用增加和故意干扰信号而产生的电磁干扰，未来可能会更频繁地发生。该项目旨在解决支撑这一挑战的挑战这两种产品。 Flare Bright 开发了一项正在申请专利的技术，该技术能够使用嵌入式 DT 来估计飞机上的气流，如果地面速度已知，则可以根据该技术估计风速，反之亦然。然而，当应用于较宽的飞行包线时，该技术受到具有多种潜在解决方案的复杂“一对多”问题的阻碍。该项目旨在利用尖端数学技术，确定是否可以确定或计算出单个解决方案比其他解决方案更有可能，从而显着提高 Flare Bright 核心产品的影响力和可行性。