OPS-SAT is a 3U CubeSat launched on December 18, 2019, it is the first nanosatellite to be directly owned and operated by the European Space Agency (ESA). The spacecraft is a flying platform that is easily accessible to European industry, institutions, and individuals for rapid prototyping, testing, and validation of their software and firmware experiments in space at no cost and no bureaucracy. Equipped with a full set of sensors and actuators, it is conceived to break the “has never flown, will never fly” cycle. OPS-SAT has spearheaded many firsts with in-orbit applications of Artificial Intelligence (AI) for autonomous operations. AI is of rising interest for space-segment applications despite limited technology demonstrators on-board flying spacecrafts. Past missions have restricted AI to inferring models trained on the ground prior to being uplinked to a spacecraft. This paper presents how the OPS-SAT Flight Control Team (FCT) breaks away from this trend with various AI solutions for in-flight autonomy. Three on-board case studies are presented: 1) image classification with Convolutional Neural Network (CNN) model inferences using TensorFlow Lite, 2) image clustering with unsupervised learning using k-means, and 3) supervised learning to train a Fault Detection, Isolation, and Recovery (FDIR) model using online machine learning algorithms. CNN inference with TensorFlow Lite running on-board the spacecraft showcases in-space application of an industry standard open-source solution originally developed for terrestrial edge and mobile computing. Furthermore, the solution is “openable” with an inference pipeline that can be constructed from crowdsourced trained models. This mechanism enables open innovation methods to extend on-board ML beyond its original mission requirement while stimulating knowledge transfer from established AI communities into space applications. Further classification is achieved by re-using an open-source k-means algorithm to cluster images in groups of “cloudiness” and initial results in image segmentation (feature extraction) have promising outlooks. Results from training a FDIR model to protect the spacecraft's camera lens against direct exposure to sunlight are presented, achieving balanced accuracies ranging from 85% to 99% from models trained with the Adagarad RDA, AROW, and NHERD online ML algorithms in multi-dimensional input spaces using photodiode diagnostics data stream as training data. The ability to train models in-flight with data generated on-board without human involvement is an exciting first that stimulates a significant rethink on how future missions can be designed.
OPS-SAT Spacecraft Autonomy with TensorFlow Lite, Unsupervised Learning, and Online Machine Learning
05.03.2022
5021086 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Future of Spacecraft Autonomy with Deep Learning
NTRS | 2021
|DEPLOYING DEEP LEARNING IN OPENFOAM WITH TENSORFLOW
TIBKAT | 2021
|Deploying deep learning in OpenFOAM with TensorFlow
AIAA | 2021
|