Using Positioner Calibration

The positioner calibration procedure surmises the best fit base (x, y, z, yaw, pitch, roll) and tool (x, y, z) parameters for a positioner sub-device (with respect to the parent robot device) based on an experimental procedure using the real robot and positioner.

Before you begin: The calibration procedure consists of selecting a robot device and:
  • its positioner's base part
  • the calibration fixture device
  • two tag groups
The fixture represents a pristine location in the workcell and the robot device is assumed to have been calibrated with respect to it.
  1. In the Calibration toolbar, click Positioner Calibration .
  2. In either the work area or in the tree, select the robot device.
  3. Select the positioner device to be adjusted.
  4. Select another device to be used as the calibration fixture device.
  5. Once the calibration fixture device is selected, the system prompts you to select two tag groups. In the work area or in the tree, select the two appropriate tag groups.

    The Tool Parameters dialog box is displayed. If the selected positioner has multiple utool/tool profiles, the displayed dialog box allows you to select the utool needing adjustment. This is the starting point for the tool calibration.

  6. In the Tool Parameters dialog box, accept the default values or assign new ones. Click OK.
    The Define Calibration Parameters dialog box appears.
  7. Accept the defaults or assign new parameters, as desired. Click OK to begin the calibration.
    When the calibration is successfully completed, the Calibration Results dialog box appears displaying an analysis of the results. When the calibration is successfully completed, the Calibration Results dialog box appears displaying an analysis of the results.
    Value Explanation
    Maximum uncertaintiesThis value represents the maximum of the uncertainties for the fit on the parameters to be identified. Large uncertainty values are an indication that the experimental observation strategy is flawed, even if the RMS fitting error is small.
    Number of iterations to convergenceThe number of iterations required by the numerical identification method.
    Number of fitting tag points The number of points used for the least squares fitting procedure.
    Root mean square fitting error The root mean square fitting error on the points after adjusting the tool profile to the best fit possible.