  On-orbit spacecraft inspection is an important capability for enabling
servicing and manufacturing missions and extending the life of spacecraft.
However, as space operations become increasingly more common and complex,
autonomous control methods are needed to reduce the burden on operators to
individually monitor each mission. In order for autonomous control methods to
be used in space, they must exhibit safe behavior that demonstrates robustness
to real world disturbances and uncertainty. In this paper, neural network
controllers (NNCs) trained with reinforcement learning are used to solve an
inspection task, which is a foundational capability for servicing missions. Run
time assurance (RTA) is used to assure safety of the NNC in real time,
enforcing several different constraints on position and velocity. The NNC and
RTA are tested in the real world using unmanned aerial vehicles designed to
emulate spacecraft dynamics. The results show this emulation is a useful
demonstration of the capability of the NNC and RTA, and the algorithms
demonstrate robustness to real world disturbances.
