Good Match between “Stop-and-Go” Strategy and Robust Guidance Based on Deep Reinforcement Learning

This paper deals with the guidance problem of close approaching small celestial bodies while autonomously navigating with an optical camera. A combination of a deep reinforcement learning (DRL)-based guidance method and a “Stop-and-Go” (SaG) strategy is here proposed to increase the mission adaptability. Firstly, a robust guidance strategy optimizing fuel consumption and angle-only navigation (AON) observability is trained by DRL. Secondly, the SAG strategy is designed to introduce the mission adaptability and further improve the AON observability. Thirdly, a good match between the SAG strategy and the DRL-based robust guidance is demonstrated. The proposed method was tested in a typical R-bar approaching scenario. Then, the mission adaptability with an onboard application was successfully verified, investigating the policy performance with SAG