Adaptive visual servoing in uncalibrated environments.

Wang Hesheng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-73).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivContents --- p.vList of Figures --- p.viiList of Tables --- p.viiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Visual Servoing --- p.1Chapter 1.1.1 --- Position-based Visual Servoing --- p.4Chapter 1.1.2 --- Image-based Visual Servoing --- p.5Chapter 1.1.3 --- Camera Configurations --- p.7Chapter 1.2 --- Problem Definitions --- p.10Chapter 1.3 --- Related Work --- p.11Chapter 1.4 --- Contribution of This Work --- p.15Chapter 1.5 --- Organization of This Thesis --- p.16Chapter 2 --- System Modeling --- p.18Chapter 2.1 --- The Coordinates Frames --- p.18Chapter 2.2 --- The System Kinematics --- p.20Chapter 2.3 --- The System Dynamics --- p.21Chapter 2.4 --- The Camera Model --- p.23Chapter 2.4.1 --- Eye-in-hand System --- p.28Chapter 2.4.2 --- Eye-and-hand System --- p.32Chapter 3 --- Adaptive Image-based Visual Servoing --- p.35Chapter 3.1 --- Controller Design --- p.35Chapter 3.2 --- Estimation of The Parameters --- p.38Chapter 3.3 --- Stability Analysis --- p.42Chapter 4 --- Simulation --- p.48Chapter 4.1 --- Simulation I --- p.49Chapter 4.2 --- Simulation II --- p.51Chapter 5 --- Experiments --- p.55Chapter 6 --- Conclusions --- p.63Chapter 6.1 --- Conclusions --- p.63Chapter 6.2 --- Feature Work --- p.64Appendix --- p.66Bibliography --- p.7

Adaptive Neuro-Filtering Based Visual Servo Control of a Robotic Manipulator

This paper focuses on the solutions to flexibly regulate robotic by vision. A new visual servoing technique based on the Kalman filtering (KF) combined neural network (NN) is developed, which need not have any calibration parameters of robotic system. The statistic knowledge of the system noise and observation noise are first given by Gaussian white noise sequences, the nonlinear mapping between robotic vision and motor spaces are then on-line identified using standard Kalman recursive equations. In real robotic workshops, the perfect statistic knowledge of the noise is not easy to be derived, thus an adaptive neuro-filtering approach based on KF is also studied for mapping on-line estimation in this paper. The Kalman recursive equations are improved by a feedforward NN, in which the neural estimator dynamic adjusts its weights to minimize estimation error of robotic vision-motor mapping, without the knowledge of noise variances. Finally, the proposed visual servoing based on adaptive neuro-filtering has been successfully implemented in robotic pose regulation, and the experimental results demonstrate its validity and practicality for a six-degree-of-freedom (DOF) robotic system which the hand-eye without calibrated

Experimental study on visual servo control of robots.

Lam Kin Kwan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 67-70).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Visual Servoing --- p.1Chapter 1.1.1 --- System Architectures --- p.2Chapter 1.1.1.1 --- Position-based Visual Servoing --- p.2Chapter 1.1.1.2 --- Image-based Visual Servoing --- p.3Chapter 1.1.2 --- Camera Configurations --- p.4Chapter 1.2 --- Problem Definition --- p.5Chapter 1.3 --- Related Work --- p.6Chapter 1.4 --- Contribution of This Work --- p.9Chapter 1.5 --- Organization of This Thesis --- p.10Chapter 2. --- System Modeling --- p.11Chapter 2.1 --- Coordinate Frames --- p.11Chapter 2.2 --- System Kinematics --- p.13Chapter 2.3 --- System Dynamics --- p.14Chapter 2.4 --- Camera Model --- p.15Chapter 2.4.1 --- Eye-in-hand Configuration --- p.18Chapter 2.4.2 --- Eye-and-hand Configuration --- p.21Chapter 3. --- Adaptive Visual Servoing Control --- p.24Chapter 3.1 --- Controller Design --- p.24Chapter 3.2 --- Parameter Estimation --- p.27Chapter 3.3 --- Stability Analysis --- p.30Chapter 4. --- Experimental Studies --- p.34Chapter 4.1 --- Experimental Setup --- p.34Chapter 4.1.1 --- Hardware Setup --- p.34Chapter 4.1.2 --- Image Pattern Recognition --- p.35Chapter 4.1.3 --- Experimental Task --- p.36Chapter 4.2 --- Control Performance with Different Proportional Gains and Derivative Gains --- p.39Chapter 4.3 --- Control Performance with Different Adaptive Gains --- p.41Chapter 4.4 --- Gravity Compensator --- p.50Chapter 4.5 --- Control Performance with Previous Image Positions --- p.51Chapter 4.6 --- Kinematic Controller --- p.56Chapter 5. --- Conclusions --- p.61Chapter 5.1 --- Conclusions --- p.61Chapter 5.2 --- Future Work --- p.62Appendix --- p.63Bibliography --- p.6