Machine learning-enabled satellites for Agile Space Operations

Space is becoming increasingly congested, contested, and competitive due to increased economic access to orbit, launches of distributed satellite systems, and conjunction events due to debris and conflict. For future satellite missions, on-board algorithms that can detect, track, identify, and characterise events and hazards that occur on-orbit will be required for space activities. One activity, autonomous maneuvering, is yet to be fully realised with the latest developments in artificial intelligence. Ability to test these
algorithms on the ground is limited and the capability does not exist in Australia. This project will commence with the development of a facility, and develop novel machine perception, navigation, guidance, and machine learning algorithms on space-based hardware.


Project Leader:
Dr Feras Dayoub, The University of Adelaide

PhD Student:
Harrison Bennett, The University of Adelaide