Abstract
The main purpose of visual servoing is to control the motion of a robot system based on visual information provided by one or more cameras. It is an important research topic in the robotics community. In uncalibrated visual servoing, the image Jacobian matrix estimation is of great importance to the success of visual servoing control. This paper addresses the online estimation of the total Jacobian matrix for robot visual servoing using the unscented particle filter. We first give the definition of the total Jacobian matrix and formulate the total Jacobian matrix estimation problem into Bayesian filtering framework. Then, we propose to estimate the total Jacobian matrix using the unscented particle filter. Each particle is propagated and updated using the unscented Kalman filter equations. Such an update can make full use of the image feature measurements and consequently generate more accurate estimation results. The simulation results on a 2DOF robot visual servoing platform show that the proposed method provides accurate and reliable performance in the object tracking task.
Highlights
Visual servoing is an important research topic in the robotics area. It refers to controlling the motion of a robot by utilizing the computer vision data acquired from one or multiple cameras mounted on the end-effector [1], [2]
SIMULATION RESULTS we use a 2 DOF robot system which is equipped with a camera on the end-effector to do the simulation experiments in order to show the performance of the proposed method
In this paper, we introduced the unscented particle filter to estimate online the total image Jacobian matrix for robot visual servoing
Summary
Visual servoing is an important research topic in the robotics area. It refers to controlling the motion of a robot by utilizing the computer vision data acquired from one or multiple cameras mounted on the end-effector [1], [2]. The final aim of visual servoing is to drive the robot to finish specific tasks including positioning, tracking, etc. PBVS needs an estimate of the object pose with respect to the camera frame, using the complete 3D model of the object. This method depends heavily on the camera calibration. In IBVS, image based visual features are extracted and directly used in the control law along with the image Jacobian to generate the control signal [3]
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