Including AI experiments onboard the UPMSat-3 satellite mission

Abstract Artificial Intelligence (AI) techniques are being used in general-purpose industrial computing systems. There is a great interest in expanding its use across other types of systems. However, they are not immediately applicable to embedded safety-critical systems. In particular, in spacecraf...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 2716; no. 1; pp. 12101 - 12108
Main Authors Pérez-Muñoz, Ángel-Grover, Alonso, Alejandro, Pérez, María S., Zamorano, Juan, Valente, Hugo, de la Puente, Juan A., Porras-Hermoso, Angel, Bayón-Laguna, Montserrat
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2024
Subjects
Artificial intelligence
Machine learning
Neural networks
Onboard equipment
Safety critical
Subsystems
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Summary:Abstract Artificial Intelligence (AI) techniques are being used in general-purpose industrial computing systems. There is a great interest in expanding its use across other types of systems. However, they are not immediately applicable to embedded safety-critical systems. In particular, in spacecrafts, there are subsystems with high integrity requirements, which means that their failure could affect the overall behavior of the vehicle or even the loss of the complete mission. This paper deals with the use of some relevant AI techniques onboard space systems. Machine Learning and Neural Networks are potential techniques for these systems. The objective of this paper is to evaluate its applicability, select the most appropriate tools, and determine its feasibility to place onboard the satellite. Through the analysis of standards proposals, and a thermal estimation use case, we identify the issues, challenges, and guidelines to be considered for the use of AI, specifically machine learning, in UPMSat-3.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2716/1/012101