An approach to optimise the critical sensor locations in one-dimensional novel distributive tactile surface to maximise performance

The distributive approach to tactile sensing is a novel approach. The method relies on the distributed deformation of the surface in response to the applied load to a few sensing points within the surface area. The description of the contacting load is then interpreted into meaningful descriptors typically by using a neural network or fuzzy rules. The method has been shown to interpret descriptors such as load position, load value and load width and relies on strong coupling between the sensory data retrieved. This opposes the design aims in many alternative tactile sensing systems that formulate load description from isolated discrete data detected over an array of sensing elements, or that delineate force descriptions through a structure that minimises coupling on Cartesian axes. For distributive tactile sensors, the performance can be optimised through placement of sensing points such that the obtained information is optimal. This paper examines the effect on performance of sensor location points on an experimental one-dimensional surface designed for this purpose. The algorithm interpreting load descriptors was a back-propagation neural network. The critical parameter of sensor location is optimised using the genetic algorithm (GA) and principal component analysis (PCA) approach. It is shown that when an optimised configuration is used load position can be predicted to within 5% of the full range by using as few as two sensing elements, and that performance is improved by using additional sensor points. The results of this study are a basis for selecting sensor locations to achieve high performance with planar one- and two-dimensional distributive tactile surfaces.