A Neuro-Fuzzy Visual Servoing Controller for an Articulated Manipulator

The challenges of selecting appropriate image features, optimizing complex nonlinear computations, and minimizing the approximation errors always exist in visual servoing. A fuzzy neural network controller is developed for a six-degrees-of-freedom robot manipulator to perform visual servoing is proposed to tackle these problems. To increase the accuracy of the image preprocesses, a synthetic image process performs feature extraction for the controller. The method combines a support vector machine contour recognition algorithm and a color-based feature recognition algorithm. For visual servoing, a control method based on the fuzzy cerebellar model articulation controller with the Takagi-Sugeno framework is proposed to directly map an image feature error vector to a desired robot end-effector velocity. This approach achieves visual servoing control without the need of computing the inverse interaction matrix. The control variables are learned and updated by the T-S fuzzy inference. This simplifies the implementation of visual servoing in real-Time applications. The proposed control method is used to control an articulated manipulator with an eye-in-hand configuration. The results of simulations and experiments demonstrate that the proposed visual servoing controller has good performance, in terms of stability and convergence.