Improving the Absolute Accuracy by Online Interpolation Technique of Industrial Robots

Abstract

This paper describes a novel method to improve the accuracy of industrial robot by calibration technique. Robot calibration is broadly classified into model-based and modeless techniques. In this paper, a modeless technique capable of interpolating the deviation along the Z-axis is defined which doesn’t consider any kinematic model, instead adapts interpolation method to compensate errors. The robot’s deviation is measured with a laser tracker and the deviation controlling parameters are calculated. The controlling parameters interpolate the deviation and with an online compensation program this deviation is compensated in real time scenario. This modeless technique is experimentally tested and it demonstrates a substantial improvement in the accuracy of the robot.

A novel, low-cost technique for modelless calibration of a 3-axis parallel kinematic machine

“…A combined model-based and modelless calibration approach was presented by Guo et al (2015), for serial industrial robots, to compensate geometric and non-geometric errors (caused by clearance in gears and bearings, hysteresis, backlash, etc.). In the work of Patel et al (2019), modelless compensation is applied to an industrial robot performing a milling operation, using a compensation robot program based on the measured errors. In Cai et al (2018), modelless calibration is performed using kriging, and compensation is applied in the controller command.…”

“…A combined model-based and modeless calibration approach was presented by Guo et al, 2015 for serial industrial robots, to compensate geometric and non-geometric errors. Other papers propose non-parametric approaches, where the robot kinematic parameters are not modified during the calibration, instead the modification is made in the controller command (Cai et al, 2018;Cao et al, 2019;Patel et al, 2019). Online error compensation is a further possibility, which utilizes servoing techniques to improve accuracy (Jiang et al, 2016).…”