Automatic navigation of wall following mobile robot using Adaptive Resonance Theory of Type-1

The automatic navigation of wall following robot is playing important role in various real world tasks such as underwater exploration, unmanned flight, and in automotive industries based on its computational complexity. In this work, a novel navigation approach based on biologically inspired neural network, known as “Adaptive Resonance Theory-1” which was proposed by Carpenter and Grossberg, has been implemented and investigated for navigation of wall following mobile robots. The proposed navigation algorithm is successfully tested with three sensor reading datasets obtained from clockwise navigation of SCITOS G5 mobile robot. Test decision accuracy (%), and simulation time were used as performance analysis parameters for the proposed algorithm and it has been found that the present work can achieve 99.59% of maximum decision accuracy.