Optical Bench Validation of Meta‐Reinforcement Learning for Image‐Based Spacecraft Guidance and Navigation

ABSTRACT This paper focuses on testing and validating meta‐reinforcement learning algorithms for image‐based spacecraft guidance, navigation and control (GNC) using an optical bench designed to mimic the computing hardware and camera systems of small spacecraft. The optical bench setup is described in detail, including the hardware used, the workflow for GNC network deployment, the camera calibration procedures and the image processing pipeline employed to correct and enhance raw camera images. The analysis assesses, across one toy problem and two real‐world space mission scenarios, the effectiveness and robustness of the GNC network when processing actual camera images in place of the simulated images used for the network training. Also, it aims to evaluate the network computational performance on typical onboard computing hardware.