Paolo Cirrincione Paze
Spacecraft Collision Avoidance: a Transformer-based Reinforcement Learning Approach.
Rel. Manuela Battipede, Luigi Mascolo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Aerospaziale, 2025
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) | Preview |
Abstract
The development of the space economy and the ever-growing interest toward space has led to the progressive congestion of the most commercially viable Earth orbits. More and more satellites are launched around our planet each year, increasing the risk of collisions between space objects that have the potential of creating millions of debris and an even more dangerous orbital environment. The necessity to develop collision avoidance tools and techniques has never been more pressing, as spacecrafts have to perform avoidance maneuvers with increasing frequency. In this scenario, trajectory optimization becomes of paramount importance, in order to avert collisions in the most effective way.
This research proposes an implementation of a Deep Reinforcement Learning framework to optimize the path of a satellite orbiting our planet in a low Earth orbit and confronted with multiple collision warnings
Tipo di pubblicazione
URI
![]() |
Modifica (riservato agli operatori) |
