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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
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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