Neural Net Robotics Visual Servo : Learning The Epipolar Geometry

Studies have shown that computations of visual kinematics relations are highly complicated and do require a considerable amount of time. This is due to dependent on Jacobians and massive inter-related relations. This hinders even complicated visual servo algorithm for real-time applications. In this respect, the proposed methodology is based on approximating the highly nonlinear relations and epipolar geometry relating changes of object visual features to changes in joint space of a robotics arm system, which are usually expressed in terms of kinematics relations, in addition to time-dependent Jacobian matrix. Artificial Neural system have been proposed for that purpose. A supervised learning artificial neutral network have been employed for learning the visual nonlinear kinematics relations. For validation, the concept have been applied to the well known Rives visual servo algorithm [1], with Two Scenes Epipolar Geometry. Results have shown that, highly accurate visual serving was achieved, while considerable amount of time has been reduced with the proposed methodology