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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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