  A large number of robotic and human-assisted missions to the Moon and Mars
are forecast. NASA's efforts to learn about the geology and makeup of these
celestial bodies rely heavily on the use of robotic arms. The safety and
redundancy aspects will be crucial when humans will be working alongside the
robotic explorers. Additionally, robotic arms are crucial to satellite
servicing and planned orbit debris mitigation missions. The goal of this work
is to create a custom Computer Vision (CV) based Artificial Neural Network
(ANN) that would be able to rapidly identify the posture of a 7 Degree of
Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can
easily identify if an arm is pointing in some general direction. The Sawyer
robotic arm is used for developing and training this intelligent algorithm.
Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to
cover the entire joint configuration space. In this work, orthogonal arrays are
used, similar to the Taguchi method, to efficiently span the joint space with
the minimal number of training images. This ``optimally'' generated database is
used to train the custom ANN and its degree of accuracy is on average equal to
twice the smallest joint displacement step used for database generation. A
pre-trained ANN will be useful for estimating the postures of robotic
manipulators used on space stations, spacecraft, and rovers as an auxiliary
tool or for contingency plans.
