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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.
This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration – Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm – which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots’ positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.
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!
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.
This book presents the proceedings of the 4th International Conference of IFToMM ITALY (IFIT), held in Naples, Italy on September 7-9, 2022. It includes peer-reviewed papers on the latest advances in mechanism and machine science, discussing topics such as biomechanical engineering, computational kinematics, the history of mechanism and machine science, gearing and transmissions, multi-body dynamics, robotics and mechatronics, the dynamics of machinery, tribology, vibrations, rotor dynamics and vehicle dynamics. A valuable, up-to-date resource, it offers an essential overview of the subject for scientists and practitioners alike, and will inspire further investigations and research.
The misalignment error during assembly and/or manufacturing error of robot linkages maycompromise the accuracy of a serial manipulator, causing the actual forward kinematics model parameter to differ from the designed value. This demands the need for forward kinematics calibration. To identify the actual model parameter, the calibration process involves recording datafrom a set of robot poses as input to a parameter identification algorithm. In order to save time on the data acquisition, optimal design of experiment is employed to use the least amount of data points. Also, to avoid unnecessary calculations and numerical issues during parameter identification, identifiability analysis is performed to determine and eliminate unidentifiable parameters. Lastly, this work proposes novel nested algorithm to improve computation efficiency and robustness for parameter identification. This approach exploits the fact that part of the model parameter can be obtained explicitly given the rest of the parameters. This effectively reduces the parameters to find during the iterative identification process and is shown to be more efficient and robust than the commonly used generic method in the literature. The calibration method is applied to a common 6-DOF industrial robot and a customarilydesigned and fabricated 4-DOF surgical robot. For the industrial robot, the nested algorithm demonstrates better robustness, faster error convergence over iteration, and less calculation time than the commonly used generic algorithm. For the surgical robot, the results for optimally designed data points demonstrate a faster convergence of residual error over the number of data points compared to randomly designed data points.
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.
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.