Performance comparison of novel WNN approach with RBFNN in navigation of autonomous mobile robotic agent
2016
This paper addresses the performance comparison of Radial Basis Function
Neural Network (RBFNN) with novel Wavelet Neural Network (WNN) of designing
intelligent controllers for path planning of mobile robot in an unknown
environment. In the proposed WNN, different types of activation functions
such as Mexican Hat, Gaussian and Morlet wavelet functions are used in the
hidden nodes. The neural networks are trained by an intelligent supervised
learning technique so that the robot makes a collision-free path in the
unknown environment during navigation from different starting points to
targets/goals. The efficiency of two algorithms is compared using some MATLAB
simulations and experimental setup with Arduino Mega 2560 microcontroller in
terms of path length and time taken to reach the target as an indicator for
the accuracy of the network models.
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