Agile Space-Observation Satellite Task Scheduling Optimisation

Agile Space-Observation Satellite Task Scheduling Optimisation

Silvagni, Pietro (TU Delft Aerospace Engineering)

Gehly, S. (mentor)
Buinhas, L. (graduation committee)
Dirkx, D. (graduation committee)

Delft University of Technology

Aerospace Engineering

2024-06-14

The congestion of orbits around the Earth, particularly in Low Earth Orbit (LEO), necessitates the implementation of space-based Space Situational Awareness (SSA) missions. This increasing orbital congestion, driven by the proliferation of mega constellations, poses significant challenges to satellite operations and collision avoidance. In response, commercial entities are beginning to plan and launch space-based SSA missions. Despite this emerging interest, there is a notable gap in the literature regarding the scheduling of operations for these missions, which are characterised by the inclusion of multiple operational modes.

Additionally, existing literature on operational scheduling often lacks high-fidelity modelling of onboard resource dynamics. This research addresses this gap by focusing on energy-constrained scenarios, developing novel operational scheduling frameworks suitable for assigning data collection and data downlink tasks for optical space-based SSA missions under such constraints.

To enhance the fidelity of the scheduling process, this research employs both low-fidelity and high-fidelity simulators to model the dynamic behaviour of onboard resources. The developed frameworks are tested in various scenarios to validate their practicality and effectiveness. A significant contribution of this research is the open-source availability of part of the developed scheduling frameworks, utilising software accessible under the TU Delft license. This open-source approach ensures that the research can be extended and built upon by future researchers, fostering ongoing advancements in the field of space-based SSA operations.

Space Situational Awareness
Satellite Task Scheduling
Resource Management and Scheduling

http://resolver.tudelft.nl/uuid:2a51d319-a33b-44a9-ad3a-5ea1e03aa964

Student theses

master thesis

© 2024 Pietro Silvagni