The two major components of a robotic tactile sensing system are the tactile sensing hardware at the lower level, and the computational/software tools at the higher level. Focusing on the later, this research assesses the suitability of Computational Intelligence tools for tactile data processing. In this context, this paper addresses the classification of sensed object material from the recorded tactile data. For this purpose, three computational intelligence paradigms, namely, Support Vector Machine (SVM), Regularized Least Square (RLS) and Regularized Extreme Learning Machine (RELM) have been employed and their performance compared for the said task. The comparative analysis shows that SVM provides the best trade-off between classification accuracy and computational complexity of the classification algorithm. Experimental results indicate that the Computational Intelligence tools are effective in dealing with the challenging problem of material classification.

Tactile Sensing Data Classification by Computational Intelligence

Dahiya, Ravinder Singh;
2011

Abstract

The two major components of a robotic tactile sensing system are the tactile sensing hardware at the lower level, and the computational/software tools at the higher level. Focusing on the later, this research assesses the suitability of Computational Intelligence tools for tactile data processing. In this context, this paper addresses the classification of sensed object material from the recorded tactile data. For this purpose, three computational intelligence paradigms, namely, Support Vector Machine (SVM), Regularized Least Square (RLS) and Regularized Extreme Learning Machine (RELM) have been employed and their performance compared for the said task. The comparative analysis shows that SVM provides the best trade-off between classification accuracy and computational complexity of the classification algorithm. Experimental results indicate that the Computational Intelligence tools are effective in dealing with the challenging problem of material classification.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/26869
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