  Current practice for asteroid close proximity maneuvers requires extremely
accurate characterization of the environmental dynamics and precise spacecraft
positioning prior to the maneuver. This creates a delay of several months
between the spacecraft's arrival and the ability to safely complete close
proximity maneuvers. In this work we develop an adaptive integrated guidance,
navigation, and control system that can complete these maneuvers in
environments with unknown dynamics, with initial conditions spanning a large
deployment region, and without a shape model of the asteroid. The system is
implemented as a policy optimized using reinforcement meta-learning. The
spacecraft is equipped with an optical seeker that locks to either a terrain
feature, back-scattered light from a targeting laser, or an active beacon, and
the policy maps observations consisting of seeker angles and LIDAR range
readings directly to engine thrust commands. The policy implements a recurrent
network layer that allows the deployed policy to adapt real time to both
environmental forces acting on the agent and internal disturbances such as
actuator failure and center of mass variation. We validate the guidance system
through simulated landing maneuvers in a six degrees-of-freedom simulator. The
simulator randomizes the asteroid's characteristics such as solar radiation
pressure, density, spin rate, and nutation angle, requiring the guidance and
control system to adapt to the environment. We also demonstrate robustness to
actuator failure, sensor bias, and changes in the spacecraft's center of mass
and inertia tensor. Finally, we suggest a concept of operations for asteroid
close proximity maneuvers that is compatible with the guidance system.
