Modular Neural Network Recognition System for Robot Endeffector Recognition 


Vol. 29,  No. 5, pp. 618-626, May  2004


PDF
  Abstract

In this paper, we describe a robot endeffector recognition system based on a Modular Neural Networks (MNN). The proposed recognition system can be used for vision system which track a given object using a sequence of images from a camera unit. The main objective to achieve with the designed MNN is to precisely recognize the given robot endeffector and to minimize the processing time.
Since the robot endeffector can be viewed in many different shapes in 3- D space, a MNN structure, which contains a set of feedforwared neural networks, can be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar characteristics so that they can be easily trained.
The trained MNN is les s sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

J. Shin and D. Park, "Modular Neural Network Recognition System for Robot Endeffector Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 5, pp. 618-626, 2004. DOI: .

[ACM Style]

Jin-wook Shin and Dong-sun Park. 2004. Modular Neural Network Recognition System for Robot Endeffector Recognition. The Journal of Korean Institute of Communications and Information Sciences, 29, 5, (2004), 618-626. DOI: .

[KICS Style]

Jin-wook Shin and Dong-sun Park, "Modular Neural Network Recognition System for Robot Endeffector Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 5, pp. 618-626, 5. 2004.

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880