Speaker
Description
Specific technological innovations are required to accomplish more ambitious commercial and scientific goals for space missions. One of the key areas of potential innovation is mission autonomy: an increased degree of on-board autonomy helps in implementing more effective mission operations. In particular, functionalities like event detection, autonomous planning and goal management, if implemented on-board, introduce several benefits to the way operations are managed:
- spacecraft are able to focus on specific, interesting targets autonomously;
- the decision making loops in the Mission Control Centres, that often introduce delays, can be avoided
- data sent to ground can be prioritized and selected considering the objectives of the missions
The characteristic that enables the presented level of autonomy is the ability to extract useful information from the data generated on the spacecraft, directly on-board. At AIKO, we develop Artificial Intelligence on-board algorithms to automate spacecraft operations, and the present work explores the use of Deep Learning to enable state of the art E4 autonomy on small satellite class missions. The paper will provide an overview of the algorithms currently under development (Convolutional and Recurrent Neural Networks), presenting the use cases (observation missions, telemetry processing) and presenting an evaluation of the performances of the algorithms, including compatibility with current and future on-board processing units.
| Paper submission | Yes |
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