A Neural Controller for Image-Based Visual Servoing of Manipulators with Physical Constraints

Main issues in visual servoing of manipulators mainly include rapid convergence of feature errors to zero and the safety of joints regarding joint physical limits. To address the two issues, in this paper, an image-based visual servoing scheme is proposed for manipulators with an eye-in-hand configuration. Compared with existing schemes, the proposed one does not require performing pseudoinversion for the image Jacobian matrix or inversion for the Jacobian matrix associated with the forward kinematics of the manipulators. Theoretical analysis shows that the proposed scheme not only guarantees the asymptotic convergence of feature errors to zero but also the compliance with joint angle and velocity limits of the manipulators. Besides, simulation results based on a PUMA560 manipulator with a camera mounted on the end effector verify the theoretical conclusions and the efficacy of the proposed scheme.