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.
2023, AII 2022: The Use of Artificial Intelligence for Space Applications, Pages 101-115
Deep Reinforcement Learning for Pin-Point Autonomous Lunar Landing: Trajectory Recalculation for Obstacle Avoidance (02a Capitolo o Articolo)
Ciabatti Giulia, Spiller Dario, Daftry Shreyansh, Capobianco Roberto, Curti Fabio
ISBN: 978-3-031-25754-4; 978-3-031-25755-1