Non-Geometrical Parameters Identification for Robot Kinematic Calibration by use of Neural Network Techniques

This paper presents a new technique for the calibration of robots based on a neural network approach for the identification of non-geometrical errors. Identification of geometrical errors is not a problem any more since several methods have been presented recently. The remaining problem is the identification of the non-geometrical errors. Non-geometrical errors modeling is a very complex and heavy process. The originality of this paper is the use of a neural network approach avoiding explicit modeling of this kind of errors. Simulations have been carried out on a robot with 6 degrees of freedom. Finally, two compensation algorithms are presented, based on the improved knowledge of the model: the first one is based on the construction of false target, the second one compensates directly into the joint space.