论文标题
基于机器学习的方法，用于对绝对旋转编码器的线性化和错误补偿
Machine learning-based method for linearization and error compensation of an absolute rotary encoder
论文作者
论文摘要
这项工作的主要目的是开发一个小型，高精度，单转的绝对旋转编码器，称为Astras360。它的测量原理基于捕获独特识别旋转角度的图像。为了评估该角度，必须先根据其颜色将图像分类为其扇区，然后才能将角度回归。在机器学习中，我们构建了一个校准设置，能够自动生成标记的培训数据。我们使用这些训练数据来测试，表征和比较几种分类和回归的机器学习算法。在另一个实验中，我们还表征了旋转编码器对偏心安装的耐受性。我们的发现表明，各种算法可以以高准确性和可靠性执行这些任务。此外，提供额外的输入（例如旋转方向）允许机器学习算法补偿旋转编码器的机械缺陷。
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.
