An improved high-precision subdivision algorithm for single-track absolute encoder using machine vision techniques

Abstract

In order to achieve high-precision and robust measurement for a single-track absolute encoder, an improved subdivision algorithm based on machine vision technology is proposed. First, the composite subdivision algorithm combining RANdom SAmple Consensus and least square estimation is introduced. Second, the proposed algorithm is proved to be high-precision and effective to remove fault error from signal noise and spot by simulation. Finally, real test results show that the algorithm output angle stably achieves precision within 2.5 arc-seconds and accuracy up to 1.6 arc-seconds. Research results provide a high-precision and robust subdivision algorithm that is improved by modern machine vision technology of RANdom SAmple Consensus and it can be applied in the field of absolute angle sensor in the future.