A new method for autonomous robot calibration

Abstract

A new method for autonomous robot calibration is presented which is suitable for on-site calibration in an industrial application environment. Using a trigger probe as an extension of the manipulator link, robot internal joint sensor measurements were recorded for kinematic identification while the robot tip-point was in contact with a constraint plane in its workspace. From the consistency conditions of the constraint plane, the linear identification equations were derived, from which the kinematic parameters were extracted based on only robot internal joint readings without any external measurements of the endpoint locations. A recurrent neural network (RNN) approach was applied to resolve the linear identification problem. The RNN-based algorithm is computationally more robust and efficient compared with conventional numerical optimisation approaches. Both simulation and experimental results for a six degree-of-freedom (DOF) PUMA robot are presented in this paper.