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Kinematic parameter estimation technique for calibration and repeatability improvement of articulated arm coordinate measuring machines

机译:运动参数估计技术,用于铰接臂坐标测量机的校准和重复性改进

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摘要

The kinematic modeling of articulated arm coordinate measuring machines (AACMM) has inherited both the previous developments in the field of robot arms and manipulators, and their calibration and parameter identification techniques, given the similarity of their mechanical characteristics. The different accuracy and repeatability requirements of both systems make it necessary to consider different identification techniques covering the characteristic operational parameters in each case. This paper presents a new data capture technique for subsequent identification of an AACMM kinematic model parameters, using nominal data reached by a ball bar gauge, along with the algorithm and objective functions used, based on a new approach including terms regarding measurement accuracy and repeatability. Thus, an estimation and error measurement correction method using a repeatability error model based on Fourier polynomials is derived from the identification scheme. A Sterling series FARO arm with 1.5 m long and 6 degrees of freedom (dof) was used to carry out experimental tests in order to evaluate the efficiency of the techniques presented, showing improved accuracy and repeatability performance.
机译:铰接臂坐标测量机(AACMM)的运动学模型继承了机械臂和机械手领域的先前发展,以及它们的校准和参数识别技术,这是由于它们的机械特性相似。两种系统对精度和重复性的要求不同,因此有必要考虑覆盖每种情况下特性操作参数的不同识别技术。本文介绍了一种新的数据捕获技术,该技术可使用球杆压力表获得的名义数据以及所使用的算法和目标函数,基于一种新的方法(包括有关测量精度和重复性的术语),用于随后识别AACMM运动学模型参数。因此,从识别方案中导出使用基于傅立叶多项式的可重复性误差模型的估计和误差测量校正方法。使用1.5 m长和6自由度(dof)的Sterling系列FARO臂进行实验测试,以评估所介绍技术的效率,从而显示出更高的准确性和可重复性。

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