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Project | ML4SCA

Duration:

Machine Learning for Satellite Collision Avoidance

Research Topics

Application fields

  • Other

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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Contact Person

Prof. Dr. Prof. h.c. Andreas Dengel Prof. Dr. Prof. h.c. Andreas Dengel

Keyfacts

Involved research areas

Publications about the project

  1. First results of ESA’s collision risk estimation and automated mitigation (CREAM) programme

    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.