Influences of sensing positions on principal components and performance of a one-dimensional distributive tactile sensor
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.