Uncalibrated Visual Tracking Control Without Visual Velocity

• Published in: IEEE transactions on control systems technology Vol. 18; no. 6; pp. 1359 - 1370
• Main Authors: Wang, Hesheng, Liu, Yun-Hui, Chen, Weidong
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Applied sciences; Artificial intelligence; Cameras; Computer science; control theory; systems; Control theory. Systems; Convergence; Drives; Errors; Exact sciences and technology; Image sampling; Linkage mechanisms, cams; Manipulator dynamics; Manipulators; Mechanical engineering. Machine design; Modelling and identification; Noise measurement; Pattern recognition. Digital image processing. Computational geometry; Programmable control; Robot arms; Robot vision systems; Robotics; Robots; Tracking; tracking control; Trajectories; Velocity control; Velocity measurement; Visual; visual servoing; visual velocity; Dynamic response; Target tracking; Tracking task; Visual servoing; Adaptive control; Robotics; Manipulator; tracking control; visual velocity; Sampling rate; Observer; System identification; Motion control; Measurement error; Visual control; Lyapunov function
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2010.2041457

This paper presents a new adaptive controller for image-based tracking of a robot manipulator without using visual velocity when the intrinsic and extrinsic parameters of the camera are not calibrated. Most of existing controllers require the measurement of the visual velocity, which is subject to big noises in general due to low sampling rates of the vision loop. To avoid performance decaying caused by measurement errors of the visual velocity we propose a new image-based tracking controller based on the proposal of an estimator of visual velocity. With a full consideration of dynamic responses of the robot manipulator, we proved the convergence of the image errors of the trajectory to zero and the convergence of the estimated parameters to the real values up to a scale by the Lyapunov method. Experiments have been conducted to demonstrate good convergence of the trajectory errors under the control of the proposed method.