A Comparative Study between Analytic and Estimated Image Jacobian by Using a Stereoscopic System of Cameras

This paper describes a comparative study of performance between the estimated image Jacobian that come from taking into account the epipolar geometry in a system of two cameras, and the well known analytic image Jacobian that is utilized for most applications in visual servoing. Image Based Visual Servoing architecture is used for controlling a 3 DOF articular system using two cameras in eye to hand configuration. Tests in static and dynamic cases were carried out, and showed that the performance of estimated Jacobian by using the properties of the epipolar geometry is such as good and robust against noise as the analytic Jacobian. This fact is considered as an advantage because the estimated Jacobian does not need laborious previous work prior to control task in contrast to the analytic Jacobian does

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

Automated pick-up of suturing needles for robotic surgical assistance

Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis

Image Based Visual Servoing: Estimated Image Jacobian by Using Fundamental Matrix VS Analytic Jacobian

This paper describes a comparative study of performance between the estimated image Jacobian that come from taking into account the geometry epipolar of a system of two cameras, and the well known analytic image Jacobian that is utilized for most applications in visual servoing. Image Based Visual Servoing architecture is used for controlling a 3 d.o.f. articular system using two cameras in eye to hand configuration. Tests in static and dynamic cases were carried out, and showed that the performance of estimated Jacobian by using the properties of the epipolar geometry is such as good and robust against noise as the analytic Jacobian. This fact is considered as an advantage because the estimated Jacobian does not need laborious previous work prior the control task in contrast to the analytic Jacobian does

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Alignment using an uncalibrated camera system

We describe a method for the visual control of a robotic system which does not require the formulation of an explicit calibration between image coordinates and the world coordinates. By extracting control information directly from the image, we free our technique from the errors normally associated with a fixed calibration. We attach a camera system to a robot such that the camera system and the robot's gripper rotate simultaneously. As the camera system rotates about the gripper's rotational axis, the circular path traced out by a point-like feature projects to an elliptical path in image space. We gather the projected feature points over part of a rotation and fit the gathered data to an ellipse. The distance from the rotational axis to the feature point in world space is proportional to the size of the generated ellipse. As the rotational axis gets closer to the feature, the feature's projected path will form smaller and smaller ellipses. When the rotational axis is directly above the object, the trajectory degenerates from an ellipse to a single point. We demonstrate the efficacy of the algorithm on the peg-in-hole problem

Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors

Model free visual servoing in macro and micro domain robotic applications

This thesis explores model free visual servoing algorithms by experimentally evaluating their performances for various tasks performed both in macro and micro domains. Model free or so called uncalibrated visual servoing does not need the system (vision system + robotic system) calibration and the model of the observed scene, since it provides an online estimation of the composite (image + robot) Jacobian. It is robust to parameter changes and disturbances. A model free visual servoing scheme is tested on a 7 DOF Mitsubishi PA10 robotic arm and on a microassembly workstation which is developed in our lab. In macro domain, a new approach for planar shape alignment is presented. The alignment task is performed based on bitangent points which are acquired using convex-hull of a curve. Both calibrated and uncalibrated visual servoing schemes are employed and compared. Furthermore, model free visual servoing is used for various trajectory following tasks such as square, circle, sine etc. and these reference trajectories are generated by a linear interpolator which produces midway targets along them. Model free visual servoing can provide more exibility in microsystems, since the calibration of the optical system is a tedious and error prone process, and recalibration is required at each focusing level of the optical system. Therefore, micropositioning and three di erent trajectory following tasks are also performed in micro world. Experimental results validate the utility of model free visual servoing algorithms in both domains