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A Generalized Kinematic Error Modeling Method for Serial Industrial Robots Based on Product of Exponentials Formula

机译:基于指数公式乘积的串行工业机器人广义运动误差建模方法

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Geometric errors such as inaccurate link length and assembly alignment are the primary sources of positioning errors for industrial robots. Besides, complex joint-dependent kinematic errors in the bearing system and harmonic drives are also non-negligible. The robot is regarded as an ideal rigid body in typical kinematic models, which can only describe the influence of geometric errors. This paper proposes a generalized kinematic error model based on product of exponentials (POE) formula, which contains constant geometric errors and complex joint-dependent kinematic errors. The unknown model parameters are identified with the Levenberg-Marquardt method. Experiments are implemented on an Efort ECR5 robot to validate the effectiveness of the proposed model. In these experiments, we use 250 measurements as the identification data set for parameter identification, and other 100 measurements are utilized to validate the accuracy of the proposed model. These experiments display that the proposed model can reduce the mean position error of the Efort ECR5 robot from 2.014 mm to 0.115 mm on the validation data set. Experimental results prove that the proposed model can describe the kinematics of industrial robots with high accuracy.
机译:诸如不准确的链路长度和装配对准之类的几何误差是工业机器人定位误差的主要源。此外,轴承系统和谐波驱动器中的复杂的关节依赖运动误差也是不可忽略的。机器人被认为是典型运动模型中的理想刚体,其只能描述几何误差的影响。本文提出了一种基于指数(POE)公式的乘积的广义运动误差模型,其包含恒定的几何误差和复杂的关节依赖性运动误差。未知的模型参数用Levenberg-Marquardt方法识别。实验在ECORECECR5机器人上实施,以验证所提出的模型的有效性。在这些实验中,我们使用250测量作为参数识别的识别数据设置,并且使用其他100测量来验证所提出的模型的准确性。这些实验显示,所提出的模型可以将ECOR5机器人的平均位置误差降低到验证数据集的2.014 mm至0.115 mm。实验结果证明,拟议的模型可以高精度地描述工业机器人的运动学。

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