In the robotics area, visual tracking is an important and difficult problem therefore is necessary to have a robust and efficient control algorithm which presents immunity characteristics to stochastic direction and speed changes of the object to be tracked. Also is important count with a segmentation algorithm which be able to tolerate changes in the intensity of light. We describe in this report the implementation of fuzzy controllers based on the fuzzy condensed algorithm and also the developed of a LVQ neural network to segment the image. For this work we used two fuzzy condensed algorithms running in a PC to control a robot’s head which tracks a human face. We describe the main lines of the fuzzy condensed algorithm as well as the LVQ neural networks architecture employed and the implementation, the fuzzy condensed controller performance in comparison to a PID controller and real time results.
