Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

Abstract

The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three (x-, y-, and z-) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration.

Keywords

Industrial robot positioning accuracy kinematic parameters calibration intelligent optimization algorithm laser tracker

Get full access to this article

View all access options for this article.

References

Kubela

Pochyly

Singule

Investigation of position accuracy of industrial robots and online methods for accuracy improvement in machining processes. In: 2015 International conference on electrical drives and power electronics (EDPE), Tatranska Lomnica, Slovakia, 21–23 September 2015, pp.385–388. New York, NY: IEEE.

Novák

Stoszek

Vyskočil

Calibrating industrial robots with absolute position tracking system. In: 2020 25th IEEE International conference on emerging technologies and factory automation (ETFA), Vienna, Austria, 8–11 September 2020, pp.1187–1190. New York, NY: IEEE.

Yang

Chan

, et al. Geometric parameter identification of a dual-arm robot by using closed-chain constraint and optimization technique. Proc IMechE, Part C: J Mechanical Engineering Science 2018; 232: 1294–1302.

Xuan

SH.

Review on kinematics calibration technology of serial robots. Int J Precis Eng Manuf 2014; 15: 1759–1774.

Abderrahim

Whittaker

AR.

Kinematic model identification of industrial manipulators. Robot Cim-Int Manuf 2000; 16(1): 1–8.

Majarena

Santolaria

Samper

, et al. Computational model for the control, performance evaluation, and calibration of a parallel mechanism. Int J Adv Manuf Technol 2013; 69: 1971–1979.

Yang

, et al. Research on path calculation and simulation system of variable parameter manipulator. In: 3rd International conference on applied mathematics, modeling and simulation (AMMS), Shanghai, China, 20–21 September 2020. New York, NY: IEEE.

Santolaria

Conte

Ginés

Laser tracker-based kinematic parameter calibration of industrial robots by improved CPA method and active retroreflector. Int J Adv Manuf Technol 2013; 66: 2087–2106.

Bai

Mei

Huang

, et al. Kinematic calibration of delta robot using distance measurements. Proc IMechE, Part C: J Mechanical Engineering Science 2016; 230: 414–424.

10.

Jiang

Fang

Gao

A multilevel index optimization method for fast kinematic calibration configuration of serial manipulators based on compute unified device architecture parallel computing. Proc IMechE, Part C: J Mechanical Engineering Science 2020; 234: 4708–4724.

11.

Wang

Dong

Bai

, et al. A new approach of kinematic geometry for error identification and compensation of industrial robots. Proc IMechE, Part C: J Mechanical Engineering Science 2019; 233: 1783–1794.

12.

Jiang

Zhou

, et al. A new kind of accurate calibration method for robotic kinematic parameters based on the extended Kalman and particle filter algorithm. IEEE Trans Ind Electron 2018; 65: 3337–3345.

13.

Huo

Xiong

Guo

, et al. A homogeneous cloud task distribution method based on an improved leapfrog algorithm. Comput Model Eng Sci 2021; 128: 359–379.

14.

Fan

Zhao

, et al. Calibration of a parallel mechanism in a serial-parallel polishing machine tool based on genetic algorithm. Int J Adv Manuf Technol 2015; 81: 27–37.

15.

Chen

Zhan

The kinematic calibration of an industrial robot with an improved beetle swarm optimization algorithm. IEEE Robot Autom Lett 2022; 7: 4694–4701.

16.

Zhong

, et al. Kinematic calibration method for a two-segment hydraulic leg based on an improved whale swarm algorithm. Robot Cim-Int Manuf 2019; 59: 361–372.

17.

Gao

Liu

San

, et al. Hybrid optimal kinematic parameter identification for an industrial robot based on BPNN-PSO. Complexity 2018; 2018: 1–11.

18.

Wang

Song

Yan

, et al. A universal index and an improved PSO algorithm for optimal pose selection in kinematic calibration of a novel surgical robot. Robot Cim-Int Manuf 2018; 50: 90–101.

19.

Das

Suganthan

PN.

Differential evolution: A survey of the state-of-the-art. IEEE Trans Evol Comput 2011; 15: 4–31.

20.

del Valle

Venayagamoorthy

Mohagheghi

, et al. Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 2008; 12: 171–195.

21.

Lipowski

Lipowska

Roulette-wheel selection via stochastic acceptance. Physica A 2012; 391: 2193–2196.

22.

Patil

Kulkarni

Aswale

Analysis of the inverse kinematics for 5 DOF robot arm using D-H parameters. In: 2017 IEEE International conference on real-time computing and robotics (RCAR), Okinawa, Japan, 14–18 July 2017, pp.688–693. New York, NY: IEEE.

23.

Zheng

Shuai

, et al. Kinematics analysis and trajectory planning of 6-DOF robot. In: 2019 IEEE 3rd Information technology, networking, electronic and automation control conference (ITNEC), Chengdu, China, 15–17 March 2019, pp.1749–1754. New York, NY: IEEE.

24.

Meng

Yuan

Liu

, et al. Forward and inverse kinematic of continuum robot for search and rescue. Adv Mater Res 2013; 712–715: 2290–2295.

25.

Zhu

Zhang

An efficient numerical method for forward kinematics of parallel robots. IEEE Access 2019; 7: 128758–128766.

26.

Zhang

Wang

, et al. An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J 2013; 2013: 130134.

27.

Jiang

Huang

, et al. An improved particle swarm optimization algorithm. Appl Math Comput 2007; 193: 231–239.

28.

Nubiola

Bonev

IA.

Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot Cim-Int Manuf 2013; 29: 236–245.

29.

Ounsrimuang

Nootyaskool

Roulette wheel selection applied to PSO on numerical function in discrete and continuous space. In: 2016 IEEE Region 10 symposium (TENSYMP), Bali, Indonesia, 9–11 May 2016, pp.361–366. New York, NY: IEEE.