A New Calibration Method for Industrial Robot Based on Step-Size Levenberg-Marquardt Algorithm

TL;DR

This paper introduces a novel calibration method for industrial robots combining an unscented Kalman filter with a variable step-size Levenberg-Marquardt algorithm, significantly improving calibration accuracy in high-precision manufacturing.

Contribution

It proposes a new calibration approach that addresses local optima and measurement noise, enhancing accuracy over existing methods.

Findings

Achieves higher calibration accuracy than state-of-the-art methods

Effectively reduces influence of measurement noise

Addresses local optima in calibration algorithms

Abstract

Industrial robots play a vital role in automatic production, which have been widely utilized in industrial production activities, like handling and welding. However, due to an uncalibrated robot with machining tolerance and assembly tolerance, it suffers from low absolute positioning accuracy, which cannot satisfy the requirements of high-precision manufacture. To address this hot issue, we propose a novel calibration method based on an unscented Kalman filter and variable step-size Levenberg-Marquardt algorithm. This work has three ideas: a) proposing a novel variable step-size Levenberg-Marquardt algorithm to addresses the issue of local optimum in a Levenberg-Marquardt algorithm; b) employing an unscented Kalman filter to reduce the influence of the measurement noises; and c) developing a novel calibration method incorporating an unscented Kalman filter with a variable step-size Levenberg-Marquardt algorithm. Furthermore, we conduct enough experiments on an ABB IRB 120 industrial robot. From the experimental results, the proposed method achieves much higher calibration accuracy than some state-of-the-art calibration methods. Hence, this work is an important milestone in the field of robot calibration.