Appearance-based visual learning in a neuro-fuzzy model for fine-positioning of manipulators

This paper presents an implementation of visual learning by appearance in conjunction with an adaptive nonlinear controller for fine-positioning a manipulator onto a grasping position. We use principal component analysis to reduce the dimension of raw camera images (about 10,000 pixels) to lower-dimension vectors that can be used as inputs of our neuro-fuzzy controllers. It is shown that this approach leads to a very robust system that is stable under variable environment conditions. The approach needs no camera calibration and is applied to tasks of three degrees of freedom, e.g., translating the gripper in the x-y-plane and rotating it about the z-axis.