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的空气流动性（AAM）软件公司，开发机器学习（ML）使用高级数字双胞胎（DT）增强了软件生态系统，它将启用安全，可靠，自主性超越视觉范围的视觉线（BVLOS）飞行（BVLOS）飞行。首先，Flare Bright建立了高分辨率的“无传感器”风测量能力，将无人机本身用作传感器。其他解决方案（例如皮托探针）较不准确，响应速度较慢，并且从大小和重量的角度看不到。在乌克里（Ukri）未来的飞行项目Safezone中，在加的夫机场（Cardiff Airport）证明了这一独特的能力，并且在ICAS 2022技术文件中审查了所达到的准确性。这种测量能力的准确性和解决方案使其成为更安全的飞行中的关键推动力，允许更快的飞行控制调音并生成风数据，这将使在城市或复杂的流动环境中定义安全的操作限制，在该环境中，飞机经历的风条件与当前可用的平均预测数据可用。当使用现有的，小且廉价的播播传感器拒绝或损坏GP时。现有的高精度INS大型，重型且消耗大量功率，使其不适合未来的无人机和电动飞机，而重量和功率至关重要。因此，我们的能力是BVLOS的关键，因为GPS退出已经定期阻碍了运行，并且将来可能会更频繁地发生由于技术使用增加而导致的电磁干扰和故意淘汰了破坏信号。该项目旨在应对这两种产品的挑战。 Flare Bright开发了一种待处理技术，该技术可以使用嵌入式DT估算飞机上的气流，如果知道地面速度，则可以从中估算出风速，反之亦然。但是，当在宽阔的飞行包膜上应用时，该技术会受到复杂的“一对一”问题，并带有多个潜在解决方案。使用尖端数学技术，该项目旨在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，从而显着提高了两种耀斑Brigh的核心产品的影响和生存能力。