  Cameras are rapidly becoming the choice for on-board sensors towards space
rendezvous due to their small form factor and inexpensive power, mass, and
volume costs. When it comes to docking, however, they typically serve a
secondary role, whereas the main work is done by active sensors such as lidar.
This paper documents the development of a proposed AI-based (artificial
intelligence) navigation algorithm intending to mature the use of on-board
visible wavelength cameras as a main sensor for docking and on-orbit servicing
(OOS), reducing the dependency on lidar and greatly reducing costs.
Specifically, the use of AI enables the expansion of the relative navigation
solution towards multiple classes of scenarios, e.g., in terms of targets or
illumination conditions, which would otherwise have to be crafted on a
case-by-case manner using classical image processing methods. Multiple
convolutional neural network (CNN) backbone architectures are benchmarked on
synthetically generated data of docking manoeuvres with the International Space
Station (ISS), achieving position and attitude estimates close to 1%
range-normalised and 1 deg, respectively. The integration of the solution with
a physical prototype of the refuelling mechanism is validated in laboratory
using a robotic arm to simulate a berthing procedure.
