Uncalibrated visual servo control with neural networkKlobučar, Rok (Avtor) Čas, Jure (Avtor) Šafarič, Riko (Avtor) Brezočnik, Miran (Avtor) robotsneural networksvisual servoingparallel manipulatorsResearch into robotics visual servo systems is an important content in the robotics field. This paper describes a control approach for a robotics manipulator. In this paper, a multilayer feedforward network is applied to a robot visual servo control problem. The model uses new neural network architecture and a new algorithm for modifying neural connection strength. No a-prior knowledge is required of robot kinematics and camera calibration. The network is trained using an end-effector position. After training, performance is measured by having the network generate joint-angles for arbitrary end effector trajectories. A 2-degrees-of-freedom (DOF) parallel manipulator was used for the study. It was discovered that neural networks provide a simple and effective way of controlling robotic tasks. This paper explores the application of a neural network for approximating nonlinear transformation relating to the robotćs tip-position, from the image coordinates to its joint coordinates. Real experimental examples are given to illustrate the significance of this method. Experimental results are compared with a similar method called the Broyden method, for uncalibrated visual servo-control.= Association of Mechanical Engineers and Technicians of Slovenia et al.20082015-07-10 18:09:37Delo ni kategorizirano52680sl