A FRAMEWORK FOR ACTIVE VISION-BASED ROBOT CONTROL USING NEURAL NETWORKS

Assembly robots that use an active camera system for visual feedback c an achieve greater flexibility, including the ability to operate in an uncertain and changing environment. Incorporating active vision into a robot control loop involves some inherent difficulties, including ca libration, and the need for redefining the servoing goal as the camera configuration changes. In this paper, we propose a novel self-organiz ing neural network that learns a calibration-free spatial representati on of 3D point targets in a manner that is invariant to changing camer a configurations. This representation is used to develop a new framewo rk for robot control with active vision. The salient feature of this f ramework is that it decouples active camera control from robot control . The feasibility of this approach is established with the help of com puter simulations and experiments with the University of Illinois Acti ve Vision System (UIAVS).