- 1INAF - IAPS, Rome, Italy
- 2INGV, Rome, Italy
- 3Department of Mathematics, CmPA, KU Leuven, Belgium
- 4KTH Royal Institute of Technology, Stockholm, Sweden
- 5Università di Trento, Trento, Italy
- 6LPP - CNRS, Paris, France
The Automatics in SpAce exploration (ASAP) project has as a goal the design and development of Machine Learning algorithms for the automation of operations to be implemented on the on-board processors of space missions. In the framework of ASAP a set of ML algorithms for on-board science operations of space missions have been developed/optimized on consumer-grade computing systems to be further selected for orting of existent ML models directly on an FPGA prototype. In more detail, algorithms pertaining to four main use cases have been considered: the autonomous triggering of special measurement modes and the selective downlink of plasma environment parameters; the advanced on-board data analysis of three-dimensional particle distribution functions; the on-board analysis of solar images; the on-board prediction capability of SEP related hazards. Here we describe the algorithms, their performances and requirements for the on-board implementation. ASAP has received funding from the EU’s HORIZON Research and Innovation Action (GA no.101082633)
How to cite: Torda, T., Alberti, T., Consolini, G., De Marco, R., Dineva, E., Ekelund, J., Gonidakis, P., Laurenza, M., Marcucci, M. F., Markidis, S., Miloshevich, G., Poedts, S., Sanò, B., and Chrysaphi, N.: Machine Learning Algorithms for Autonomous Space Mission Operations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16713, https://doi.org/10.5194/egusphere-egu25-16713, 2025.
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