Intelligent visual servoing with extreme learning machine and fuzzy logic

While visual servoing (VS) provides the ability of motion using vision for robot manipulators, the approaches for a better VS have to deal with three common problems: obtaining the interaction matrix and its pseudoinverse for defined feature points, finding an appropriate gain value for the VS controller and keeping the features in the field of view (FOV) for VS permanency. In this study, a new intelligent image-based visual servoing (IBVS) system for eye-in-hand configured robot manipulators using extreme learning machine (ELM) and fuzzy logic (FL) is proposed to solve these common problems of VS in a single system. As the first stage of the system, the pseudoinverse of the interaction matrix is approximated using trained ELMs which do not need hidden layer tuning. As the second stage, the classical IBVS controller is modified by a differential equation regarding initial velocity continuity and an appropriate gain in each loop is assigned by an FL unit to provide fast convergence within velocity limits. This unit also promotes manipulability of the manipulator to avoid singularities. As the last stage of the proposed system, regions are defined in the image plane to take precautions before feature missing. When a feature comes close to the edge of a restricted region, an FL unit is activated to obtain negative linear velocities in x and y direction which will be added to the instant velocities to drag the features towards the center of the FOV. In addition to these abilities, some VS metrics are redefined analytically to standardize the performance metric definitions of VS. To show the performance of the proposed system, simulation results of the classical and the proposed IBVS system under practical disturbances are presented for visual servoing of a Puma 560 arm. The advantages of singular matrix and joint configuration avoidance, adaptive gain with smooth gain surface, decreased convergence time within velocity limits, initial velocity continuity, FOV keeping with smooth velocity assurance, redefined VS metrics for standardization and robustness against disturbances are proved by variety of simulations. The simulation results also verify that the proposed system utilizing intelligent methods like ELM and FL is capable of dealing with common problems of VS and achieves sufficient results in terms of VS metrics. (C) 2016 Elsevier Ltd. All rights reserved.