This work aims to present a method to perform autonomous precision landing—pin-point landing—on a planetary environment and perform trajectory recalculation for fault recovery where necessary. In order to achieve this, we choose to implement a Deep Reinforcement Learning—DRL—algorithm, i.e. the Soft Actor-Critic—SAC—architecture. In particular, we select the lunar environment for our experiments, which we perform in a simulated environment, exploiting a real-physics simulator modeled by means of the Bullet/PyBullet physical engine. We show that the SAC algorithm can learn an effective policy for precision landing and trajectory recalculation if fault recovery is made necessary—e.g. for obstacle avoidance.
Deep Reinforcement Learning for Pin-Point Autonomous Lunar Landing: Trajectory Recalculation for Obstacle Avoidance / Ciabatti, Giulia; Spiller, Dario; Daftry, Shreyansh; Capobianco, Roberto; Curti, Fabio. - (2023), pp. 101-115. - STUDIES IN COMPUTATIONAL INTELLIGENCE. [10.1007/978-3-031-25755-1_7].
Deep Reinforcement Learning for Pin-Point Autonomous Lunar Landing: Trajectory Recalculation for Obstacle Avoidance
Ciabatti, Giulia;Spiller, Dario;Capobianco, Roberto;Curti, Fabio
2023
Abstract
This work aims to present a method to perform autonomous precision landing—pin-point landing—on a planetary environment and perform trajectory recalculation for fault recovery where necessary. In order to achieve this, we choose to implement a Deep Reinforcement Learning—DRL—algorithm, i.e. the Soft Actor-Critic—SAC—architecture. In particular, we select the lunar environment for our experiments, which we perform in a simulated environment, exploiting a real-physics simulator modeled by means of the Bullet/PyBullet physical engine. We show that the SAC algorithm can learn an effective policy for precision landing and trajectory recalculation if fault recovery is made necessary—e.g. for obstacle avoidance.File | Dimensione | Formato | |
---|---|---|---|
Ciabatti_Deep_2023.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
546.26 kB
Formato
Adobe PDF
|
546.26 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.