Abstract
Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that predict, for example, the evolution of the collision risk over time. In October 2019, in an attempt to study this opportunity, the European Space Agency released a large curated dataset containing information about close approach events in the form of conjunction data messages (CDMs), which was collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, which was an ML competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying ML methods to this problem domain.
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Acknowledgements
The ESA would like to thank the Unite States Space Surveillance Network for the agreement that enabled the public release of the dataset for the objectives of the competition.
The authors would like to thank all the scientists that participated in the Spacecraft Collision Avoidance Challenge and that dedicated their time and knowledge to an important element of ESA’s operated satellites.
In particular, we would like to acknowledge all members of team sesc, whose methodology is briefly described in this paper: Steffen Limmer, Sebastian Schmitt, Viktor Losing, Sven Rebhan, and Nils Einecke.
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Thomas Uriot graduated from the University of Oxford in the UK, where he obtained his master degree in statistics and mathematics. Thomas worked as a researcher at the ESA in the Advanced Concepts Team, where he conducted research on evolutionary machine learning and spacecraft collision avoidance.
Dario Izzo graduated as a doctor of aeronautical engineering from the University Sapienza of Rome (Italy). He then took a second master degree in satellite platforms at the University of Cranfield in the UK and completed his Ph.D. degree in mathematical modelling at the University Sapienza of Rome where he lectured and space flight mechanics. Dario Izzo later joined the European Space Agency (ESA) and became the scientific coordinator of its Advanced Concepts Team. He devised and managed the Global Trajectory Optimization Competitions, the ESA’s Summer of Code in Space, and the Kelvins innovation and competition platform for space problems. He published more than 180 papers in international journals and conferences making key contributions to the understanding of flight mechanics and spacecraft control and pioneering techniques based on evolutionary and machine learning approaches. Dario Izzo received the Humies Gold Medal and led the team winning the 8th edition of the Global Trajectory Optimization Competition.
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Uriot, T., Izzo, D., Simões, L.F. et al. Spacecraft collision avoidance challenge: Design and results of a machine learning competition. Astrodyn 6, 121–140 (2022). https://doi.org/10.1007/s42064-021-0101-5
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DOI: https://doi.org/10.1007/s42064-021-0101-5