Pensiri Tongpadungrod and Jaratsri Rungrattanaubol

pp. 87 - 94

Abstract

This paper describes an arrangement of a one-dimensional distributive tactile sensing system that can be used to
determine an applied position of a point load of a constant magnitude. The performance of the system was examined using
inputs derived from a mathematical model and a back propagation neural network as an interpretation algorithm. Performances
of the system with 2–8 inputs with and without an application of principal component analysis (PCA) as a preprocessor were
examined. For each number of inputs, four sets of sensing positions were explored and the accuracies in determining an
applied load position were compared. It was found that the system was able to determine an applied load position with errors
in the range of 1.0–3.1 mm depending on the number of inputs and the method of inputting data. The error decreased with an
increase in the number of inputs. It was found that input preprocessing by PCA impaired the performance. Systematically
chosen and optimal sets of sensing positions resulted in the most desirable performance and their performances were comparable. Amongst the sets of input positions explored, random positions yielded the highest errors. Random positions also
resulted in the largest difference between the first two principal components.

Keywords

• neural network
• principal component analysis
• tactile sensing
• position determination