Deep Reinforcement Learning for Autonomous Spacecraft Inspection using
  Illumination
This paper investigates the problem of on-orbit spacecraft inspection using a
single free-flying deputy spacecraft, equipped with an optical sensor, whose
controller is a neural network control system trained with Reinforcement
Learning (RL). This work considers the illumination of the inspected spacecraft
(chief) by the Sun in order to incentivize acquisition of well-illuminated
optical data. The agent's performance is evaluated through statistically
efficient metrics. Results demonstrate that the RL agent is able to inspect all
points on the chief successfully, while maximizing illumination on inspected
points in a simulated environment, using only low-level actions. Due to the
stochastic nature of RL, 10 policies were trained using 10 random seeds to
obtain a more holistic measure of agent performance. Over these 10 seeds, the
interquartile mean (IQM) percentage of inspected points for the finalized model
was 98.82%.
David van Wijk Kyle Dunlap Manoranjan Majji Kerianne L. Hobbs