Deep Reinforcement Learning as Guidance for Aerospace Robotics

Abstract

The ability for a manipulator-equipped chaser spacecraft to autonomously capture a target spacecraft is an unsolved prerequisite for space debris removal and on-orbit servicing. This thesis investigates using deep reinforcement learning (DRL) to improve the capabilities of a manipulator-equipped chaser at this task. DRL allows for behaviour to be learned, rather than designed, according to a simple reward function.