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A global calibration scheme is proposed to resolve the
coordinates' equivalence problem in integrating the CAD system
and Robots. Current robot calibration schemes are inevitably
with certain locality, i.e., the calibrated error parameters
(CEP) will solicit the demanded accuracy only in certain region
of the robot workspace. It s mainly due to that the errors
resulting in the imprecision are not completely modeled and
only limited number of measured data are available for
identifying the CEPs. To tackle this locality problem, we
propose first performing the measurement space analysis to
appropriately divide the workspace into local regions and
select the representative set of CEPs from each local region.
Learning algorithms based on both the CMAC or FCMAC neural
networks are then employed to generate appropriate sets of CEPs
for the whole workspace based on the derived finite sets of
CEPs above. Simulation and experiment are executed to verify
the proposed global calibration scheme.
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