SCARLET-⍺: SpaceCraft Autonomy and Onboard AI for Next Generation Space Systems

 

Spacecraft autonomy has been recognised as a key enabler of the next-generation space systems that aim at increasing responsiveness and continuity of space-based observations, covering large areas with higher resolutions, minimizing communication and data access latencies, and reducing costs of both the space and ground segments.

Spacecraft autonomy encompasses onboard autonomous decision-making capabilities that enable the space segment to continue mission operations and to survive critical situations without relying on ground segment intervention. It relates to all aspects of spacecraft operations, including continuous mission planning and execution on board, real-time spacecraft control outside ground contact, maximisation of mission objectives in relation to the available onboard resources and capabilities of other spacecraft, and system robustness in presence of on-board failures and external uncertainties.

This project aims at addressing the above requirements by developing novel concepts, methods and technologies to provide new AI-based spacecraft autonomy capabilities for the next-generation space systems, such as dynamically networked formations of heterogeneous satellites. It focuses on high impact areas of spacecraft autonomy and onboard AI as identified and prioritised with the industry and defence partners, including:

  • WP1: Onboard processing and actionable intelligence
  • WP2: Small spacecraft and constellation resilience
  • WP3: Dynamic optimisation of constellation resources
  • WP4: Real-time tasking and resource allocation

The output of this project is a set of autonomous algorithms, demonstrating their capability through software simulations with use-cases provided by the industry partners.

This project will leverage and contribute to the IPC Capability Demonstrator with an aim to be demonstrated on DST RMS STaR Shot as a pathway to commercialisation of the developed technology solutions for industry partners.

P2.52

Project Leader:
Professor Ryszard Kowalczyk, University of South Australia

Participants: