A cumulative damage approach to modeling atmospheric corrosion of steel
Robot kinematic calibration is the process of enhancing the positioning accuracy of a given manipulator and must be performed after robot manufacture and assembly or during periodical maintenance. This dissertation presents 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 a conceptual 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 parameters that can be insightful in identifying redundant/irrelevant parameters and deducing the DH(-) error model of a manipulator. The method is a hybrid scheme comprised of the Simulated Annealing (SA) algorithm and a local solver/optimizer and it conducts a statistical analysis on the estimates of a given error parameter that is indicative of its relevance. For the type of industrial robots used in this dissertation, we made note that calibrating the home position only is sufficient to attain adequate results for most robotics applications. Hence, we put forward for consideration of a yet simpler calibration model; the DH(-)(-) model. We employ the Trust Region (TR) method to minimize the objective functions (solve for the error parameters of the simplified error models) of both frameworks (3DCAL and 1DCAL). We also compare the performance of the proposed methods 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 are more robust and yield better results compared to that of MotoCal.