A Novel Calibration System for Robot Arm via an Open Dataset and a Learning Perspective

Abstract

Industrial robot arms are the critical equipment of industrial production and have been widely adopted in various fields, such as the aerospace and petrochemical industry. However, an uncalibrated robot arm suffers from extremely low absolute positioning accuracy, which cannot satisfy the accuracy requirements of high-precision manufacture. To address this thorny issue, it is extremely important to implement periodic calibration for robot arms. In general, most experts on calibration systems have rich mechanical and instrument experience. However, due to the particular complexity involved in the collection of calibration data, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies. In this brief, we consider the issue of robot arm calibration from the perspective of machine learning and develop a publicly available dataset called ”RobotCali”. The main ideas of this work are four-fold: a) developing a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas, b) adopting an extended Kalman filter algorithm to suppress the measurement noises in a robot arm calibration system, c) designing an improved covariance matrix adaptive evolution strategy to achieve fast convergence rate and high searching stability, d) proposing a novel calibration system based on an extended Kalman filter and an improved covariance matrix adaptive evolution strategy. Additionally, extensive experiments demonstrate that compared with state-of-the-art calibration systems, the proposed calibration system obtains a highly competitive calibration accuracy.