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Uncalibrated visual servo control with neural network
Rok Klobučar, Jure Čas, Riko Šafarič, Miran Brezočnik, 2008, izvirni znanstveni članek

Opis: Research 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.
Ključne besede: robots, neural networks, visual servoing, parallel manipulators
Objavljeno: 10.07.2015; Ogledov: 703; Prenosov: 66
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