Implementation of a variable D-H parameter model for robot calibration using an FCMAC learning algorithm

Current robot calibration schemes usually employ calibration models with co nstant error parameters. Consequently, they are inevitably subject to a cer tain degree of locality, i.e., the calibrated error parameters (CEPs) will produce the desired accuracy only in certain regions of the robot workspace . To deal with the locality phenomenon, CEPs that vary in different regions of the robot workspace may be more appropriate. Hence, we propose a variab le D-H (Denavit and Hartenberg) parameter model to formulate variations of CEPs. An FCMAC (Fuzzy Cerebellar Model Articulation Controller) learning al gorithm is used to implement the proposed variable D-K parameter model. Sim ulations and experiments that verify the effectiveness of the proposed cali bration scheme based on the variable D-H parameter model are described.