DAASE Members Earl T. Barr and David R. White are members of a successful consortium to win highly-competitive funding from the Centre for Earth Observation Instrumentation (CEOI). This CEOI funding will enable the application of advanced AI algorithms to the next generation of nano-satellites.
Nano-satellites are a very popular trend in satellite design right now: many small low-powered satellites are built using commodity components rather than the traditional approaches that incorporate high-specced parts in the construction of larger satellites. This greatly reduces their cost and has enabled a new generation of applications, particularly through the deployment of “constellations”: groups of satellites operating in tandem to monitor phenomena such as climate change and protect against impending natural disasters.
David and Earl will be working with industrial experts at space companies Craft Prospect and Bright Ascension, alongside the University of Manchester. Their focus will be on the application of Deep Learning and Code Optimisation methods to the satellite software, taking advantage of emerging hardware platforms that offer enhanced capabilities on-board.
“In the same way that commodity hardware components revolutionised satellite construction, we believe there is potential for a revolution in the software domain,” said David, who will take responsibility for algorithm development on the project.
By applying the power and commoditised tools of machine learning that have emerged over the last decade, the researchers intend to make the satellites more autonomous, reducing the demands on the data connection to the ground station. The work will involve a combination of cutting-edge Machine Learning methods alongside the improvement of industry-standard software through methods such as Genetic Improvement.
“We’re excited to see how far we can take this,” says White, “The timing is perfect for a new wave of satellite applications enabled by AI.”