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Opto-tactile sensor for surface texture pattern identification using support vector machine

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conference contribution
posted on 06.12.2017, 00:00 authored by Abdul MazidAbdul Mazid, A B M Shawkat AliA B M Shawkat Ali
Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like Support Vector Machine (SVM) technique, Decision Tree (DT) C4.5 algorithm, and Naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

1830

End Page

1835

Number of Pages

6

Start Date

01/01/2008

ISBN-13

9781424422876

Location

Hanoi, Vietnam

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics; Faculty of Sciences, Engineering and Health; TBA Research Institute;

Era Eligible

Yes

Name of Conference

International Conference on Control, Automation, Robotics and Vision