Robust and autonomous machine learning on board a miniature satellite

微型卫星上的鲁棒自主机器学习

• 批准号: NI220100072
• 负责人: Prof James Bailey
• 金额: $ 39.34万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: National Intelligence and Security Discovery Research Grants
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/NI220100072
• 关键词: Robust; autonomous; machine; learning; board

The advent of miniature satellites (CubeSats) orbiting in space is a new and exciting frontier. There is an emerging opportunity for CubeSats to use adaptable machine learning models to perform on board analysis and processing of data. This proposal aims to research, develop and deliver state of the art approaches that tackle autonomy and robustness challenges for machine learning within a CubeSat. This will enable machine learning models to operate in the hostile and low resource environment of space. A distinguishing benefit of this project is our plan to deploy and evaluate new machine learning approaches on board a soon to be launched CubeSat, demonstrating Australia’s leadership at an international level.

在太空轨道运行的微型卫星(立方体卫星)的出现是一个令人兴奋的新领域。CubeSats正在出现使用适应性机器学习模型来执行机载数据分析和处理的机会。这项提议旨在研究、开发和交付最先进的方法，以解决CubeSat中机器学习的自主性和健壮性挑战。这将使机器学习模型能够在恶劣和低资源的太空环境中运行。该项目的一个显著好处是我们计划在即将发射的CubeSat上部署和评估新的机器学习方法，展示澳大利亚在国际层面上的领导地位。