Angle-only Autonomous Terminal Guidance and Navigation Algorithm for Asteroid Defense based on Meta-reinforcement Learning

Abstract This paper presented a robust angle-only guidance and navigation algorithm for asteroid defense missions based on meta-reinforcement learning. A recurrent neural network, trained via proximal policy optimization, is used to map the line-of-sight angles captured in real-time by the onboard camera to the optimal thrust. The neural network effectively replaces the roles of the navigation and guidance system while simultaneously removing the dependence on dynamic and observation models. The guidance and navigation model is tested on numerical simulations of a simulated mission directed to asteroid Bennu. The objective is to enable the spacecraft to hit the asteroid precisely, despite the presence of scattered initial conditions, uncertain model parameters, thruster control error, and attitude control and measurement error.