Machine Learning for Satellite Collision Avoidance

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With an increasing number of satellites and debris objects in earth orbit it becomes increasingly important to automate and improve procedures for collision avoidance. We develop machine learning tools for predicting the collision probability of satellites.

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Publications about the project

Volker Schaus; Tilman Andriof; Colin Borrett; Ingo Burmeister; Francisco Cabral; José Carvalho; Markus Daugs; Louise Hetherton; Florian Jung; Anthony de la Llave; Silvia Martinavarro; Keiran McNally; Maria Mirgkizoudi; Dinesh Krishna Natarajan; Marlon Nuske; Deepak Kumar Pathak; Ian Purton; Fabian Schiemenz; Zoe Tenacci; Benedikt Veith; Jan Siminski; Klaus Merz

In: 73rd. International Astronautical Congress (IAC-2022), 20th IAA Symposium on Space Debris, September 18-22, Paris, France, IAF, 2022.

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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz