Spacecraft collision avoidance challenge:Design and results of a machine learning competition  

作　　者：Thomas Uriot Dario Izzo Luís FSimões Rasit Abay Nils Einecke Sven Rebhan Jose Martinez-Heras Francesca Letizia Jan Siminski Klaus Merz 

机构地区：[1]The European Space Agency,Noordwijk,2201 AZ,the Netherlands [2]ML Analytics,Lisbon,Portugal [3]FuturifAI,Canberra,Australia [4]Honda Research Institute Europe GmbH,Offenbach 63073,Germany [5]ESOC,Space Debris Office,Darmstadt 64293,Germany

出　　处：《Astrodynamics》2022年第2期121-140,共20页航天动力学（英文）

摘　　要：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.

关 键 词：space DEBRIS collision avoidance COMPETITION kelvins 

分 类 号：V41[航空宇航科学与技术—航空宇航推进理论与工程]