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Uncalibrated visual servo control with neural network

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3 Author(s)
Klobucar, R. ; Fac. of Electr. Eng. & Comput. Sci. / Inst. for Robot., Univ. of Maribor, Maribor ; Cas, J. ; Šafarič, R.

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 a 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 robot's 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.

Published in:

Advanced Motion Control, 2008. AMC '08. 10th IEEE International Workshop on

Date of Conference:

26-28 March 2008