NDLI: Uncalibrated visual servo control with neural network

Content Provider	IEEE Xplore Digital Library
Author	Klobucar, R. Cas, J. Safaric, R.
Copyright Year	2008
Description	Author affiliation: Fac. of Electr. Eng. & Comput. Sci. / Inst. for Robot., Univ. of Maribor, Maribor (Klobucar, R.; Cas, J.; Safaric, R.)
Abstract	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.
Starting Page	74
Ending Page	79
File Size	788695
Page Count	6
File Format	PDF
ISBN	9781424417025
DOI	10.1109/AMC.2008.4516044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Servosystems Neural networks Robot kinematics Manipulators Servomechanisms Multi-layer neural network Robot vision systems Cameras Calibration End effectors
Content Type	Text
Resource Type	Article

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