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

Zeyin Zhao, Xin Wang, Jiafang Chen, Mengzhong Chen
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Abstract

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.
基于指数积公式的串联工业机器人运动误差广义建模方法
连杆长度和装配对中误差等几何误差是工业机器人定位误差的主要来源。此外,轴承系统和谐波传动中复杂的关节相关运动误差也是不可忽略的。在典型的运动学模型中,机器人被视为理想刚体,只能描述几何误差的影响。提出了一种基于指数积(POE)公式的广义运动学误差模型,该模型包含常几何误差和复关节相关运动学误差。采用Levenberg-Marquardt方法对未知模型参数进行辨识。最后,在Efort ECR5机器人上进行了实验,验证了该模型的有效性。在这些实验中,我们使用250个测量值作为参数识别的识别数据集,并使用其他100个测量值来验证所提出模型的准确性。实验结果表明,该模型可以将Efort ECR5机器人在验证数据集上的平均位置误差从2.014 mm减小到0.115 mm。实验结果表明,该模型能较准确地描述工业机器人的运动学。
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