Positioning error calibration of six-axis robot based on sub-identification space

Jiang, Xiaogeng; Zhang, Duo; Wang, Hao

doi:10.1007/s00170-024-12973-6

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The commonly used kinematic models include Denavit-Hartenberg (D-H) model [15,16], S-model [17], complete and parametrically continuous (CPC) model [18], etc. Jiang et al [19] presented a subspace parameter error identification model to identify the kinematic parameter errors and analyzed the sensitivity of the kinematic parameter errors of each axis of the six-axis industrial robot. Toquica et al [20] proposed a parameter calibration method by using low-cost measurement points collected in several sub-regions within the robot workspace separately and extends the effectiveness of this proposal to calibration in a large workspace.…”

Section: Introductionmentioning

confidence: 99%

Positioning error compensation method for industrial robots based on stacked ensemble learning

Chen,

Zhang,

et al. 2024

Preprint

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Due to the advantages of low cost, high flexibility and large workspace, industrial robot has been considered to be the most promising plan to replace traditional CNC machine tool. However, the low absolute positioning accuracy of robot is a key factor that restricts further application in high-precision metal cutting scenarios. In order to improve the absolute positioning accuracy of robot, a positioning error compensation method based on the stacked ensemble learning is proposed. Firstly, the sources of positioning errors and compensation strategies are clarified by analyzing the kinematic model and structural composition of industrial robot. Then, based on the stacked ensemble learning algorithm, robot positioning error prediction model containing multi-layer learners is constructed. And a discrete grid optimization method is presented for model hyper-parameters optimization calculation. Next, predicted positioning errors are adopted to the realize the positioning compensation by offline compensation method. Finally, by set up a robotic milling platform based on MOTOMAN ES165D robot, a series of error compensation experiments have been implemented to verify the proposed method. After compensation, the maximum absolute position error and average position error have decreased by 83% and 89% respectively in the compensation experiments of single point. Moreover, the error compensation of the end milling experiments has also brought significant accuracy improvement, which proved the effectiveness of the proposed method in robotic machining.

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