Represent a fuzzy cognitive mobile robot map inspired from place cells and head direction cells with dimension reduction approach

In this paper, a new model for mobile robot mapping is presented. The model is inspired by
functionality of the cells of cortex postsubiculum layer. This model is based on visual information so, robot
visual input from environment is considered as input of the model. V1 layer of visual cortex of the brain, is
modelled by Gabor filter due to extracting image texture and Gabor filter histogram is used as image
features. Therefore, the model can be used in real environments with similar colors. The output dimension of
this layer is decreased using unsupervised basic dimension reduction techniques such as Kernel-PCA, PCA,
ISOMAP and MDS. High-dimensional data suffer from problems called curse of dimensionality. By
reducing the data dimension, in addition to the reduced data volume storage, this problem is to overcome. To
the best of our knowledge, the model is the first model that was developed with the purpose of dimension
reduction. Another innovation is presentinga fuzzy clustering model. Using limited number of direction cells,
the model makes interpolate possible to find robot head direction in defuzzification step. In previous models
such as Tokonaga and Milford, output angels are limited to number of direction cells, while this constraint is
resolved in the proposed model. The output of direction cells provided by the model are similar to the actual
output of the direction cells that have been obtained from experimental tests on the brain. The
implementation results of the proposed model is evaluated and compared with other methods. In most cases
results show higher accuracy.