In-line thermal compensation of industrial robots based on binocular stereo vision

Jigui, 邾继贵 Zhu; Nan-nan, 张楠楠 Zhang; Yongjie, 任永杰 Ren; Shi-bin, 尹仕斌 Yin; Yin, 郭寅 Guo; Siyang, 郭思阳 Guo

doi:10.3788/ope.20182609.2139

Cited by 2 publications

(1 citation statement)

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“…e high-end manufacturing application of industrial robots has attracted extensive attention from research institutions [1][2][3][4]. In 2017-2019, the National Key Research and Development Project "Intelligent robot" of China pointed out that the position and attitude accuracy should be better than 0.1 mm and 0.1°, respectively, in the high-end manufacturing field.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis on the Effectiveness of Kinematic Error Compensation Methods for Serial Industrial Robots

Zhang

Qiao

Song

et al. 2021

Mathematical Problems in Engineering

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Based on the established serial 6-DOF robot calibration experiment platform, this paper aims to analyze and compare the effects of four error compensation methods, which are pseudotarget iteration-based error compensation method with three different forms and the Newton–Raphson-based error compensation method. Firstly, the pose error model of the serial robot is established based on the M-DH model in this paper. The calibration results show that the accuracy of the Staubli TX60 robot has been greatly improved. The average comprehensive position accuracy is increased by 88.7%, and the average comprehensive attitude accuracy is increased by 56.6%. Secondly, the principles of the four error compensation methods are discussed, and the effectiveness of the four error compensation methods are compared through experiments. The results show that the four error compensation methods can achieve error compensation well. The compensation accuracy is consistent with the identification accuracy of the kinematic model. The pseudotarget iteration with differential form has the best performance by the comprehensive consideration of accuracy and computational efficiency. Error compensation determines whether the accuracy of the identified model can be achieved. This paper presents a systematic experimental validation research on the effectiveness of four error compensation methods, which provides a reliable reference for the kinematic error compensation of industrial robots.

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Section: Introductionmentioning

confidence: 99%