Online path correction under end point nonholonomic constraints – Implementation with visual sensing in robotic fiber placement

Cao, Shixiu; Guo, Yingjie; Zhu, Weidong; Wang, Haijin; Huang, Qiwei; Ke, Yinglin

doi:10.1016/j.cirpj.2023.06.009

Cited by 2 publications

(1 citation statement)

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“…In load-sharing tasks, relative errors lead to internal force or even damage of the handled parts (Maghami et al , 2023). And in coordinated fiber placement, relative pose errors can result in defects such as gaps and overlaps between the tows laid by different robots (Cao et al , 2023). As a result, the academic and industrial communities have dedicated significant efforts to address this issue through robot calibration and error compensation.…”

Section: Introductionmentioning

confidence: 99%

A method for predicting relative position errors in dual-robot systems via knowledge transfer from geometric and nongeometric calibration

Cao,

Wang,

Guo

et al. 2024

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Purpose In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems. Design/methodology/approach A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor. Findings Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively. Originality/value First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

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Section: Introductionmentioning

confidence: 99%