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Angle-based Correlation (AbC), refined astrometry on board a CubeSat, Experiment 156 on OPS-SAT

AbC is a compact on-board algorithm, initially intended for CubeSats, to measure the absolute direction of a distant beacon with optimised accuracy. The AbC environment anticipates a ground pipeline that has been used with OPS-SAT images to diagnose the ADCS. It has been shown that a ground-truth assessment of the satellite's orientation can be made which, with sufficient data, could allow precise re-alignment of multiple sensors. The ESA contract funded the implementation of the on-board algorithm in C++ and its pipeline in Python.

France

21-D-S-OPS-02-l

OBSERVATOIRE DE PARIS
SmallSat DTN

Smallsat DTN is an OPS-SAT experiment awarded funding trough ESA’s OSIP, OPS-SAT campaign. The aim of the project is to investigate the benefits of CFDP and Bundle Protocol for satellite operations and their possible applications in the optical communications links, of the future ESA mission of OPS-SAT 2. The Flight Campaign of the project completed in a three-day span, successfully demonstrating the implementation of a LEO DTN network, spacecraft remote control and configuration over Bundle Protocol and CFDP imagery data distribution over multiple passes from the ground segment.

Greece

21-D-S-OPS-02-c

HELLENIC AEROSPACE...
Commercial cloud computing in space

The ESA Open Space Innovation Platform Campaign for Cognitive Cloud Computing in Space (3CS) called for project proposals for the use of edge and cloud computing technologies in space. D-Orbit created a consortium including UniBap and Trillium to respond to this opportunity with a proposal to engage the cloud computing community to identify and fly applications on the ION SCV-004 mission.

United Kingdom

EISI_I-2022-00373

D-orbit
Data compression as a service

The Data Compression as a Service (DCaaS) study aimed to bring the benefits of data compression to the experimenters on ESA's OPS-SAT platform. It focused on using the POCKET+ algorithm for the compression of housekeeping telemetry data. The project contained a space component, which performed the compression of provided user data, and a ground component, for the decompression of transmitted user data. Three use cases were selected within the scope of the project: file-to-file, packet-to-packet, and stream-to-stream lossless data transfers.

Germany

21-D-S-OPS-02-d

VisionSpace Technologies
A Comparative Study Of High-Level And Low-Level Implementations Of Deep Learning Models For Spacecraft

In this activity, Mission Control Space Services deployed a low-level implementation of the OPS-SAT SmartCam model using a Field Programmable Gate Array (FPGA), comparing against a high-level CPU model using Tensorflow Lite. Experiments showed that the FPGA implementation reproduced the precision and accuracy of the high-level model, while running at a slower speed. Further optimisations of the FPGA are expected to close the gap in timing and unlock new methods for deploying deep learning on spacecraft.

Canada

21-D-S-OPS-02-e

Mission Control Space...
AI AT THE EDGE : DEEP CUBE SERVICE IOD/IOV

ESA's OPS-SAT mission is a key element for operational validation in flight (or IOD/IOV) of the DeepCube service of Deep Learning at the edge that Agenium Space is preparing with the support of the GSTP programme (GSTP Make, ESA-CNES) and the R&D performed in the CORTEX project (Permanent Open Call EOEP-4, ESA/Phi-Lab, https://esacortexproject.agenium-space.com). The goal of this activity was to execute on board the inference of the simplified models defined in those projects.

France

21-D-S-OPS-02-i

Agenium Space
Beyond the Mission Paradigm: Federated Reconfigurable Infrastructure via Edge Devices in Space (FRIENDS)

Early space missions consisted of bespoke components communicating with facilities on Earth. Today this model has been updated with off-the-shelf components and reusable launch systems that lower mission cost into low-Earth orbit. Extending this model to develop a sustainable off-Earth economy requires bringing the concept of reconfigurable computing agents to deep space.

Canada

EISI_I-2022-00378

Mission Control Space...
DEEPCUBE

The goal of the project was to develop a service as a product to simplify fitting DNN (Deep Learning Neural Networks) in on board HW to make better use of AI on space missions The role of the service is to support data processing engineers in reducing HW resources requirement of powerful DNN for image analysis to be executed onboard. 

France

GT27-048ED

Agenium Space
Artificial Intelligence for Terrain relative Navigation in Unknown Environment (ATENA)

Space missions benefit greatly by the capability of the on-board GNC system to adapt rapidly to unknown environment. Autonomous vision-based navigation is a particular technology under implementation in several ESA missions. One of the most interesting applications in that field is the proximity operations around a small asteroid, like those in HERA.
The goal of this activity was to develop a navigation algorithm with the capability to fly over an unknown terrain and achieve better navigation performances than current vision-based techniques based on unknown feature tracking.

Spain

20-D-O-TEC-01

GMV
Real-time optimal control of quadrocopters using deep representations of the optimal state feedback

A major challenge in the field of control is to achieve reliable, aggressive, high-speed control of autonomous vehicles. In space, this may involve spacecraft that need to land under harsh conditions, or even – in an extreme scenario – negotiating asteroid debris fields at high speeds. On Earth, the exemplar task that draws most attention currently is high-speed autonomous flight of drones. The application of optimal control on board limited platforms has been severely hindered by the large computational requirements of current state-of-the-art implementations.

The Netherlands

18-8510

TU DELFT

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