Modelling Experimental Procedures For Manipulator Calibration

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 thesis discusses the kinematic calibration of the constraining linkage of a four degrees of freedom parallel manipulator. The manipulator has hybrid actuation of joints and wires, however the wires are not considered in this calibration. Two of the passive joints of the manipulator contain sensing so the calibration of the constraining linkage can be considered. Four kinematic models are developed for the manipulator. For each of these models, an independent set of model parameters are identified through an analysis of the augmented identification Jacobian matrix. Three different methods for formulating the augmented identification Jacobian matrix are explored. For the calibration, an optical tracking system is used to track the end-effector of the manipulator. The procedure to collect the calibration data is explained and the sources of error are considered. To further analyze the sources of error, simulated input data is created and the calibration using the experimental data and the simulated data are compared. In an attempt to improve the calibration, the selection of measured poses to be used for calibration is explored. Several different pose selection criteria have been proposed in the literature and five are evaluated in this work. The pose selection criteria were applied to the experimental manipulator and also a simulated two degrees of freedom manipulator. It is found that the pose selection criteria have a large impact when few poses are used; however the best results occur when a large number of poses are used for the calibration. An experimental calibration is carried out for the manipulator. Using the joint encoders and the kinematic model, the expected pose of the end-effector is calculated. The actual pose is measured using a vision tracking system and the difference between the actual and expected pose is minimized by adjusting the model parameters using a nonlinear optimization method.

Model-Based Control of a Robot Manipulator presents the first integrated treatment of many of the most important recent developments in using detailed dynamic models of robots to improve their control. The authors' work on automatic identification of kinematic and dynamic parameters, feedforward position control, stability in force control, and trajectory learning has significant implications for improving performance in future robot systems. All of the main ideas discussed in this book have been validated by experiments on a direct-drive robot arm. The book addresses the issues of building accurate robot models and of applying them for high performance control. It first describes how three sets of models - the kinematic model of the links and the inertial models of the links and of rigid-body loads - can be obtained automatically using experimental data. These models are then incorporated into position control, single trajectory learning, and force control. The MIT Serial Link Direct Drive Arm, on which these models were developed and applied to control, is one of the few manipulators currently suitable for testing such concepts. Contents: Introduction. Direct Drive Arms. Kinematic Calibration. Estimation of Load Inertial Parameters. Estimation of Link Inertial Parameters. Feedforward and Computed Torque Control. Model-Based Robot Learning. Dynamic Stability Issues in Force Control. Kinematic Stability Issues in Force Control. Conclusion. Chae An is Research Staff Member, IBM T.J. Watson Research Center, Christopher Atkeson is an Assistant Professor and John Hollerbach is an Associate Professor in the MIT Department of Brain and Cognitive Sciences and the MIT Artificial Intelligence Laboratory. Model-Based Control of a Robot Manipulator is included in the Artificial Intelligence Series edited by Patrick Winston and Michael Brady.

This book has evolved from a course on Mechanics of Robots that the author has thought for over a dozen years at the University of Cassino at Cassino, Italy. It is addressed mainly to graduate students in mechanical engineering although the course has also attracted students in electrical engineering. The purpose of the book consists of presenting robots and robotized systems in such a way that they can be used and designed for industrial and innovative non-industrial applications with no great efforts. The content of the book has been kept at a fairly practical level with the aim to teach how to model, simulate, and operate robotic mechanical systems. The chapters have been written and organized in a way that they can be red even separately, so that they can be used separately for different courses and readers. However, many advanced concepts are briefly explained and their use is empathized with illustrative examples. Therefore, the book is directed not only to students but also to robot users both from practical and theoretical viewpoints. In fact, topics that are treated in the book have been selected as of current interest in the field of Robotics. Some of the material presented is based upon the author’s own research in the field since the late 1980’s.

A general method for establishing a kinematic model of a robotic manipulator with either a full or partial pose calibration system is developed. The theory applicable to modeling of mechanisms is introduced, as is robotic manipulator calibration. Given a general over-specified kinematic model, a method is developed, with the associated algorithms for a six degree of freedom manipulator, that identifies non-unique parameter sets for any given partial pose measurement system. This method is applied and demonstrated on three existing calibration methods.

A general method for establishing a kinematic model of a robotic manipulator with either a full or partial pose calibration system is developed. The theory applicable to modeling of mechanisms is introduced, as is robotic manipulator calibration. Given a general over-specified kinematic model, a method is developed, with the associated algorithms for a six degree of freedom manipulator, that identifies non-unique parameter sets for any given partial pose measurement system. This method is applied and demonstrated on three existing calibration methods.