Computationally efficient and robust kinematic calibration methodologies and their application to industrial robots

In this paper, we present new computationally efficient and robust kinematic calibration algorithms for industrial robots that make use of partial measurements. These include a calibration method that requires the supply of Cartesian coordinates of the calibration points (3DCAL) and another calibration technique that only requires the radial measurements from the calibration points to some reference (1DCAL). Neither method requires orientation measurements nor the explicit knowledge of the where-about of a reference frame. Contrary to most other similar works, both methods make use of a simplified version of the original Denavit-Hartenberg (DH) kinematic model. The simplified DH(-) model has not only proven to be robust and effective in calibrating industrial manipulators but it is also favored from a computational efficiency viewpoint since it consists of comparatively fewer error parameters. We present an analytical approach to develop a set of guidelines that need to be considered in order to properly construct the DH(-) model such that it is parameterically continuous and non-redundant. We also propose an automated method to provide a characterization of the error parameters that is insightful so as to correctly deduce the DH(-) error model of a manipulator. The method makes use of a novel hybrid optimization scheme to conduct a statistical analysis of the error parameters that is indicative of their relevance. We made note that, for the industrial robots used in this paper and similar ones, calibrating the home position only is sufficient to attain adequate results for most robotic applications. Hence, we put forward for consideration a yet simpler calibration model; the DH(-)(-) model. We employ the Trust Region (TR) method to minimize the objective functions of both frameworks (3DCAL and 1DCAL). The performance of the proposed methods is compared to that of a state-of-the-art commercial system (MotoCal) using the same materials, data and internationally recognized performance standards. Our experimental results suggest that our methods yield improved results compared to that of MotoCal. (C) 2015 Elsevier Ltd. All rights reserved.