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| Kinematics Calibration of Industrial Robot Fusing Weighted SVD Algorithm |
| BAN Zhao1,2,REN Guo-ying2,3,WANG Bin-rui1,CHEN Xiang-jun3,XUE Zi2,WANG Ling1 |
1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. National Institute of Metrology, Beijing 100029, China
3. State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China |
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Abstract Aiming at the problem that the gross error of measurement introduced by environmental or human factors has a great influence on the conversion of measurement coordinate system and base coordinate system of robot, a method is proposed that the singular value decomposition (SVD) algorithm is improved and applied to the robot kinematics calibration. Taking ABB-IRB2600 robot as the research object, modified D-H (MD-H) kinematics model and error model were established. The position coordinates of the target sphere at the end of robot were measured by the laser tracker. In the SVD algorithm, the weight of the measured data was redistributed according to the position error before compensation, and the measurement coordinate system and the robot base coordinate system were converted. Levenberg-Marquardt (L-M) algorithm was used to identify the error parameters, and 25 kinematic parameters of the robot were simulated and compensated in Matlab. Simulation and experimental results show that the weighted SVD algorithm has better stability and can reduce the impact of gross errors. After calibration, for the average absolute error of the robot is reduced by 65.10% and the root mean square error by 65.85%, and its absolute positioning accuracy is obviously improved after calibration.
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Received: 28 July 2020
Published: 24 September 2021
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2021, Vol. 42 
