Opto-tactile sensor for surface texture pattern identification using support vector machine
conference contributionposted on 2017-12-06, 00:00 authored by Abdul MazidAbdul Mazid, A 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.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Number of Pages6
Place of PublicationUSA
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External Author AffiliationsFaculty of Business and Informatics; Faculty of Sciences, Engineering and Health; TBA Research Institute;