This paper introduces a guidance strategy for spacecraft proximity operations, which leverages deep reinforcement learning, a branch of artificial intelligence. This technique enables guidance strategies to be learned rather than designed. The learned guidance strategy feeds velocity commands to a conventional controller to track. Control theory is used alongside deep reinforcement learning to lower the learning burden and facilitate the transfer of the learned behavior from simulation to reality. In this paper, a proof-of-concept spacecraft pose tracking and docking scenario is considered, in simulation and experiment, to test the feasibility of the proposed approach. Results show that such a system can be trained entirely in simulation and transferred to reality with comparable performance.

本文介绍了航天器接近操作的指导策略，该策略利用了深度强化学习（人工智能的一个分支）。这种技术使学习指导策略而不是设计指导策略。学习的制导策略将速度命令馈送到常规控制器以进行跟踪。控制理论与深度强化学习一起使用，可降低学习负担并促进将学习到的行为从模拟转移到现实。本文在仿真和实验中考虑了概念验证的航天器姿态跟踪和对接场景，以检验该方法的可行性。结果表明，这种系统可以完全接受模拟训练，并以相当的性能转移到现实中。