  The main objective of this work is to develop a miniaturized, high accuracy,
single-turn absolute, rotary encoder called ASTRAS360. Its measurement
principle is based on capturing an image that uniquely identifies the rotation
angle. To evaluate this angle, the image first has to be classified into its
sector based on its color, and only then can the angle be regressed. In-spired
by machine learning, we built a calibration setup, able to generate labeled
training data automatically. We used these training data to test, characterize,
and compare several machine learning algorithms for the classification and the
regression. In an additional experiment, we also characterized the tolerance of
our rotary encoder to eccentric mounting. Our findings demonstrate that various
algorithms can perform these tasks with high accuracy and reliability;
furthermore, providing extra-inputs (e.g. rotation direction) allows the
machine learning algorithms to compensate for the mechanical imperfections of
the rotary encoder.
