Fuzzy membership function based neural networks with applications to the visual servoing of robot manipulators

It is shown that there exists a nonlinear mapping which transforms image features and their changes to the desired camera motion without measuring of the relative distance between the camera and the object. This nonlinear mapping can eliminate several difficulties occurring in computing the inverse of the feature Jacobian as in the usual feature-based visual feedback control methods. Instead of analytically deriving the closed form of this mapping, a fuzzy membership function (FMF) based neural network incorporating a fuzzy-neural interpolating network is proposed to approximate the nonlinear mapping, where the structure of the FMF network is similar to that of radial basis function neural network which is known to be very effective in the function approximation. Several FMF networks are trained to be capable of tracking a moving object in the whole workspace along the line of sight. For an effective implementation of the proposed FMF network, an image feature selection process is investigated, and the required fuzzy membership functions are designed. Finally, several numerical examples are presented to show the validity of the proposed visual servoing method