Abstract
This paper studies the constraint control of a visual servoing system which composed of a 6DOF manipulator with a camera sensor mounted on the end-effector subject to the limitations of the field of view (FOV). In visual servoing control, losing information about the visual features can easily lead to task failure. In this work, a barrier Lyapunov function (BLF) is utilized to design the controller and render certain features continuously stay within the field of view, also guarantee the uniform ultimate boundedness of the closed-loop system. No path planning nor information about the actual system dynamics is required. The Moore-penrose inverse term is introduced to ensure the asymptotic stability of the system. In the presence of system uncertainties, an adaptive neural network is adopted to compensate for uncertainties. The theoretical findings are demonstrated via comparative simulation studies based on a 6 DOF robot.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.