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

Abstract

Current robot calibration schemes usually employ calibration models with constant error parameters. Consequently, they are inevitably subject to a certain 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 variable D-H (Denavit and Hartenberg) parameter model to formulate variations of CEPs. An FCMAC (Fuzzy Cerebellar Model Articulation Controller) learning algorithm is used to implement the proposed variable D-K parameter model. Simulations and experiments that verify the effectiveness of the proposed calibration scheme based on the variable D-H parameter model are described.