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First Name: 
Si-Lu
Last Name: 
Chen

Datasets & Analysis

The data are used to identify the  kinematic parameters deviation of Cartesian robot, train Gaussian Process Regression (GPR) model, record the compensation result of four calibration methods under different loading conditions.

Instructions: 

Compensation results file: It expresses the compensation results in 8 test points when using four calibration methods under different loading conditions. We can see Figure16 in this paper.

HCT+BD+GPR_training file: These data record 320 groups of position points of the end effector after using HCT+BD model to compensate.  We can get 320 groups of residual error data by simply calculating the difference between these data and these designated positions. And they are used to train GPR model. 10-fold cross validation results of GPR model about x and z error are obtained by using these data. They  are shown in Figure14 and Figure15 in this paper.

 

HCT+GPR_training file: These data record 320 groups of position points of the end effector after using HCT model to compensate.  We can get 320 groups of residual error data by simply calculating the difference between these data and these designated positions. And they are used to train GPR model.

Identify_kinematic_parameter_deviation file: Using nonlinear least squares method to minimize the difference  between the amended position and actual position. We can get the deviation of kinematic parameters. The procedure to identify the deviation of kinematic parameters is shown in Figure 4. And we can see the result of deviation in Table 2 in this paper.

 

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