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

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

Enhanced Image-Based Visual Servoing Dealing with Uncertainties

Nowadays, the applications of robots in industrial automation have been considerably increased. There is increasing demand for the dexterous and intelligent robots that can work in unstructured environment. Visual servoing has been developed to meet this need by integration of vision sensors into robotic systems. Although there has been significant development in visual servoing, there still exist some challenges in making it fully functional in the industry environment. The nonlinear nature of visual servoing and also system uncertainties are part of the problems affecting the control performance of visual servoing. The projection of 3D image to 2D image which occurs in the camera creates a source of uncertainty in the system. Another source of uncertainty lies in the camera and robot manipulator's parameters. Moreover, limited field of view (FOV) of the camera is another issues influencing the control performance. There are two main types of visual servoing: position-based and image-based. This project aims to develop a series of new methods of image-based visual servoing (IBVS) which can address the nonlinearity and uncertainty issues and improve the visual servoing performance of industrial robots. The first method is an adaptive switch IBVS controller for industrial robots in which the adaptive law deals with the uncertainties of the monocular camera in eye-in-hand configuration. The proposed switch control algorithm decouples the rotational and translational camera motions and decomposes the IBVS control into three separate stages with different gains. This method can increase the system response speed and improve the tracking performance of IBVS while dealing with camera uncertainties. The second method is an image feature reconstruction algorithm based on the Kalman filter which is proposed to handle the situation where the image features go outside the camera's FOV. The combination of the switch controller and the feature reconstruction algorithm can not only improve the system response speed and tracking performance of IBVS, but also can ensure the success of servoing in the case of the feature loss. Next, in order to deal with the external disturbance and uncertainties due to the depth of the features, the third new control method is designed to combine proportional derivative (PD) control with sliding mode control (SMC) on a 6-DOF manipulator. The properly tuned PD controller can ensure the fast tracking performance and SMC can deal with the external disturbance and depth uncertainties. In the last stage of the thesis, the fourth new semi off-line trajectory planning method is developed to perform IBVS tasks for a 6-DOF robotic manipulator system. In this method, the camera's velocity screw is parametrized using time-based profiles. The parameters of the velocity profile are then determined such that the velocity profile takes the robot to its desired position. This is done by minimizing the error between the initial and desired features. The algorithm for planning the orientation of the robot is decoupled from the position planning of the robot. This allows a convex optimization problem which lead to a faster and more efficient algorithm. The merit of the proposed method is that it respects all of the system constraints. This method also considers the limitation caused by camera's FOV. All the developed algorithms in the thesis are validated via tests on a 6-DOF Denso robot in an eye-in-hand configuration