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Robot calibration is the process of identifying the real geometrical parameters in the kinematic structure of an industrial robot. This book compares different robot calibration methods used in the industry with different measurement systems (laser trackers, stereo cameras, touch probes, ...). This work introduces easier and more affordable robot calibration methods, such as calibrating robots with a telescoping ballbar. The robot calibration methods described in this book are the same methods used in RoboDK, a software tool for offline programming, robot calibration and robot performance tests, including the ISO 9283 tests.
Calibration is playing an increasingly important role in industrial robotics. Higher accuracy demands are being placed on flexible assembly and manufacturing systems which in turn require robot manufacturers to produce higher quality precision robots.
Robot calibration is the process of enhancing the accuracy of a robot by modifying its control software. This book provides a comprehensive treatment of the theory and implementation of robot calibration using computer vision technology. It is the only book to cover the entire process of vision-based robot calibration, including kinematic modeling, camera calibration, pose measurement, error parameter identification, and compensation. The book starts with an overview of available techniques for robot calibration, with an emphasis on vision-based techniques. It then describes various robot-camera systems. Since cameras are used as major measuring devices, camera calibration techniques are reviewed. Camera-Aided Robot Calibration studies the properties of kinematic modeling techniques that are suitable for robot calibration. It summarizes the well-known Denavit-Hartenberg (D-H) modeling convention and indicates the drawbacks of the D-H model for robot calibration. The book develops the Complete and Parametrically Continuous (CPC) model and the modified CPC model, that overcome the D-H model singularities. The error models based on these robot kinematic modeling conventions are presented. No other book available addresses the important, practical issue of hand/eye calibration. This book summarizes current research developments and demonstrates the pros and cons of various approaches in this area. The book discusses in detail the final stage of robot calibration - accuracy compensation - using the identified kinematic error parameters. It offers accuracy compensation algorithms, including the intuitive task-point redefinition and inverse-Jacobian algorithms and more advanced algorithms based on optimal control theory, which are particularly attractive for highly redundant manipulators. Camera-Aided Robot Calibration defines performance indices that are designed for off-line, optimal selection of measurement configurations. It then describes three approaches: closed-form, gradient-based, and statistical optimization. The included case study presents experimental results that were obtained by calibrating common industrial robots. Different stages of operation are detailed, illustrating the applicability of the suggested techniques for robot calibration. Appendices provide readers with preliminary materials for easier comprehension of the subject matter. Camera-Aided Robot Calibration is a must-have reference for researchers and practicing engineers-the only one with all the information!
Describes the details of the calibration process step-by-step, covering systems modeling, measurement, identification, correction and performance evaluation. Calibration techniques are presented with an explanation of how they interact with each other as they are modified. Shows the reader how to determine if, in fact, a robot problem is a calibration problem and then how to analyze it.
This book highlights the basic theories and key technologies of error compensation for industrial robots. The chapters are arranged in the order of actual applications: establishing the robot kinematic models, conducting error analysis, conducting kinematic and non-kinematic calibrations, and planning optimal sampling points. To help readers effectively apply the technologies, the book elaborates the experiments and applications in robotic drilling and milling, which further verifies the effectiveness of the technologies. This book presents the authors’ research achievements in the past decade in improving robot accuracy. It is straightforwardly applicable for technical personnel in the aviation field, and provides valuable reference for researchers and engineers in various robotic applications.
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.