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.
Deep Reinforcement Learning for Spacecraft Proximity Operations Guidance
Journal of Spacecraft and Rockets ; 58 , 2 ; 254-264
2021-01-14
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
ON DEEP REINFORCEMENT LEARNING FOR SPACECRAFT GUIDANCE
TIBKAT | 2020
|On Deep Reinforcement Learning for Spacecraft Guidance
AIAA | 2020
|