Camera Aided Robot 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!

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.

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.

Multi-camera systems play an increasingly important role in computer vision. They enable applications like 3D video reconstruction, motion capture, smart homes, wide area surveillance, etc. Most of these require or benefit from a calibration of the multi-camera system. This book presents a novel approach for automatically estimating that calibration. In contrast to established methods, it neither requires a calibration object nor any user interaction. From a theoretical point of view, this book also presents and solves the novel graph theoretical problem of finding shortest triangle paths.

The calibration system proposed showed to improve the robot accuracy to well below 1mm. The system allows a large variation in robot configurations, which is essential to proper calibration. A technique was used and a straightforward convention to build kinematic models for a manipulator was developed, ensuring that no singularities are present in the error model. Mathematical tools were implemented to optimize the kinematic model parameterization, avoiding redundancies between parameters and improving the parameter identification process. A portable, ease of use, speedy and reliable Vision-based measuring system using a single camera and a plane calibration board was developed and tested independently of the robot calibration process. The robot calibration system approach proposed here stood out to be a feasible alternative to the expensive and complex systems available today in the market, using a single camera and showing good accuracy and ease of use and setup. Results showed that the RAC model used (with slight modifications) is not very robust, since even for images filling the entire screen and captured at approximately the same distances from the target, the focus length was not constant and showed an average value shifted by approximately 3% from the exact one. This amount of error can produce 3-D measurement errors much larger than acceptable. Practically speaking, the solution for this problem developed here for a set of camera and lens was to use an external measurement system to calibrate the camera, at least once. The measurement accuracy obtained is comparable to the best found in academic literature for this type of system, with median values of accuracy of approximately 1:3,000 when compared to the distance from the target. However, this accuracy was obtained at considerable larger distances and different camera orientations than usual applications for cameras require, making the system suitable for robotic metrology. For future research it is suggested that the target plate and the calibration board have to be improved to permit the camera to be placed at larger ranges of distances from the target, allowing larger calibration volumes to be used. One path that might be followed is to construct a much larger calibration board, with localized clusters of calibration points of different sizes, instead of just one pattern of point distribution. So, if the camera is placed at a greater distance, larger dots can be used all over the area of the calibration board. If the camera is nearer to the target, smaller dots can be used at particular locations on the calibration board. Different dot sizes make easier for the vision processing software to recognize desired clusters of calibration points. Other sources of lens distortions such as decentering and thin prism can be also modeled, and so their influence on the final measurement accuracy can be understood. Another issue concerns the influence orientation measured data may have on the final accuracy. Non-geometric parameters such as link elasticity, gear elasticity and gear backlash might be modeled, and a larger number of parameters introduced in the model parameterization. This procedure may improve the accuracy substantially if the robot is used with greater payloads.

The majority of today s industrial robots are programmed to follow a predefined trajectory. This is sufficient when the robot is working in a fixed environment where all objects of interest are situated in a predetermined position relative to the robot base. However, if the robot s position is altered, all the trajectories have to be reprogrammed for the robot to be able to perform its tasks. The manipulator considered in this thesis is attached to an ROV (Remote Operating Vehicle). Every time the robot is repositioned, it needs to estimate its position and orientation relative to the work environment. The ROV operates at great depths and there are few sensors which can operate at extreme depths. This is the incentive for the use of computer vision to estimate the relative position of the manipulator. Through cameras, the position of the manipulator is estimated. This information is sent to controllers to correct the pre-programmed trajectories. This work includes camera calibration and calibration of the structure of the manipulator. The increased accuracies achieved by these steps are merged to achieve in-situ calibration of the manipulator base.