This Master's thesis investigates the use of the reinforcement learning algorithm Proximal Policy Optimization (PPO) for achieving a planar Autonomous Rendezvous, Proximity Operation, and Docking (ARPOD) manoeuvre with an under-actuated CubeSat. Together with the safety considerations, the different control objectives throughout the three phases reflect the complexity necessary for safe and efficient operations.
Questa tesi di Master analizza l'utilizzo dell'algoritmo di reinforcement learning Proximal Policy Optimization (PPO) per ottenere una manovra planare di Autonomous Rendezvous, Proximity Operation e Docking (ARPOD) di un under-actuate CubeSat. I diversi obiettivi di controllo durante le tre fasi, insieme alle considerazioni sulla sicurezza, riflettono la complessità necessaria per ottenere operazioni sicure ed efficienti.
Safe ARPOD for under-actuated CubeSat via reinforcement learning
PARIS, MATTHIEU VINH
2020/2021
Abstract
This Master's thesis investigates the use of the reinforcement learning algorithm Proximal Policy Optimization (PPO) for achieving a planar Autonomous Rendezvous, Proximity Operation, and Docking (ARPOD) manoeuvre with an under-actuated CubeSat. Together with the safety considerations, the different control objectives throughout the three phases reflect the complexity necessary for safe and efficient operations.File | Dimensione | Formato | |
---|---|---|---|
2021_10_Paris.pdf
accessibile in internet per tutti
Dimensione
2.17 MB
Formato
Adobe PDF
|
2.17 MB | Adobe PDF | Visualizza/Apri |
I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/10589/179340