Dipartimento di Ingegneria informatica, automatica e gestionale

The aim of this work is 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 do so, 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.