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Authors:
Thepdawala, Salman Ali; Förstner, Roger 
Document type:
Konferenzbeitrag / Conference Paper 
Title:
Towards Reinforcement Learning-Based Collision Avoidance in Low-Earth Orbit: An Initial Study 
Title of conference publication:
IAC-2023 
Organizer (entity):
International Astronautical Federation 
Conference title:
International Astronautical Congress (74., 2023, Baku) 
Venue:
Baku, Aserbaidschan 
Year of conference:
2023 
Date of conference beginning:
02.10.2023 
Date of conference ending:
06.10.2023 
Place of publication:
Paris 
Publishing institution:
International Astronautical Federation (IAF) 
Year:
2023 
Pages from - to:
79459 
Language:
Englisch 
Keywords:
Collision Avoidance ; Spacecraft Operations ; Reinforcement Learning ; Low Earth Orbit 
Abstract:
The work presented in this paper investigates the use of reinforcement learning (RL) to address the growing threat of collisions between operational spacecraft and debris in Low-Earth Orbit (LEO). With the deployment of an increasing number of mega-constellations in LEO, the manual handling of conjunction events will soon become impractical. Hence, we must look for autonomous onboard solutions to be integrated into the collision avoidance pipeline. RL is a promising AI-based algorithm for this t...    »
 
Article ID:
79459 
Department:
Fakultät für Luft- und Raumfahrttechnik 
Institute:
LRT 9 - Institut für Raumfahrttechnik und Weltraumnutzung 
Chair:
Förstner, Roger 
Research Hub UniBw M:
SPACE 
Project:
AI4COLA 
Open Access yes or no?:
Nein / No