Alberto Preti
A Deep Reinforcement Learning Framework for Autonomous and Time-Critical Collision Avoidance in Low Earth Orbit.
Rel. Paolo Maggiore, Davide Conte. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Aerospaziale, 2025
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Abstract
The increase in the population of objects in orbit, together with the exponential growth of mega constellations, is significantly increasing the risk of collisions in Low Earth Orbit (LEO). This trend makes collision avoidance crucial to prevent the generation of new debris which, triggering a chain reaction, could lead to what the Kessler syndrome predicted, compromising future access to Earth orbit. Traditional approaches to collision avoidance maneuver planning based on ground operations are not scalable to the expected future traffic volumes and are ineffective in time-critical scenarios, where the time window available to plan and implement a maneuver can be reduced to just a few hours before the time of closest approach (TCA).
Due to these challenges, this thesis proposes a framework based on Deep Reinforcement Learning for the autonomous planning of time-critical collision avoidance maneuvers in LEO, characterized by decision windows as short as 1-2 hours before TCA
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