  Autonomous navigation and path-planning around non-cooperative space objects
is an enabling technology for on-orbit servicing and space debris removal
systems. The navigation task includes the determination of target object
motion, the identification of target object features suitable for grasping, and
the identification of collision hazards and other keep-out zones. Given this
knowledge, chaser spacecraft can be guided towards capture locations without
damaging the target object or without unduly the operations of a servicing
target by covering up solar arrays or communication antennas. One way to
autonomously achieve target identification, characterization and feature
recognition is by use of artificial intelligence algorithms. This paper
discusses how the combination of cameras and machine learning algorithms can
achieve the relative navigation task. The performance of two deep
learning-based object detection algorithms, Faster Region-based Convolutional
Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using
experimental data obtained in formation flight simulations in the ORION Lab at
Florida Institute of Technology. The simulation scenarios vary the yaw motion
of the target object, the chaser approach trajectory, and the lighting
conditions in order to test the algorithms in a wide range of realistic and
performance limiting situations. The data analyzed include the mean average
precision metrics in order to compare the performance of the object detectors.
The paper discusses the path to implementing the feature recognition algorithms
and towards integrating them into the spacecraft Guidance Navigation and
Control system.
