Controlling Wall Following Robot Navigation Based on Gravitational Search and Feed Forward Neural Network

Abstract

Automated control of mobile robot navigation is a challenging area in the field of robotics research. In this work, an attempt is made to use a new neural network training algorithm based on gravitational search (GS) and feed forward neural network (FFNN) for automatic robot navigation of wall following mobile robots. The GS strategy is used for setting the optimal weight set of the FFNN so as to increase the performance of the neural network. The algorithm is tested with three large datasets obtained from UCI machine learning repository, containing a sequence of sensor readings where sensors are arranged around the waist of the SCITOS G5 robot. The proposed method shows promising results for all the datasets.