Image-Based Meta-Reinforcement Learning for Autonomous Guidance of an Asteroid Impactor

Abstract

This paper focuses on the use of meta-reinforcement learning for the autonomous guidance of a spacecraft during the terminal phase of an impact mission toward a binary asteroid system. The control policy is replaced by a convolutional-recurrent neural network, which is used to map optical observations collected by the onboard camera to the control thrust and thrusting times. The network is trained by a proximal policy optimization algorithm, a family of reinforcement learning methods. The final phase of NASA’s Double Asteroid Redirection Test (DART) mission is used as a test case. The objective is to maneuver the spacecraft to impact the smaller object, Dimorphos, in the Didymos binary system. The spacecraft dynamics are described using the bi-elliptic restricted four-body problem with solar radiation pressure. The initial conditions are randomly scattered according to the actual specifications of the DART mission. A random error on the orbital position of Dimorphos is also considered to reflect uncertainty on the binary system’s characteristics and dynamics. The control system aims at minimizing the error on the final spacecraft position. Numerical results show that the guidance system can correctly drive the spacecraft toward the final impact point in more than 98% of the 500 test scenarios.