A New Classification Technique in Mobile Robot Navigation
Abstract: This paper presents
a novel pattern recognition algorithm that use weightless neural network (WNNs)
technique.This technique plays a role of situation classifier to judge the situation
around the mobile robot environment and makes control decision in mobile robot
navigation. The WNNs technique is choosen due to significant advantages over
conventional neural network, such as they can be easily implemented in hardware
using standard RAM, faster in training phase and work with small resources.
Using a simple classification algorithm, the similar data will be grouped with
each other and it will be possible to attach similar data classes to specific
local areas in the mobile robot environment. This strategy is demonstrated in
simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM
and low cost sensors. Experimental result shows, when number of neuron
increases the average environmental recognition ratehas risen from 87.6% to
98.5%.The WNNs technique allows the mobile robot to recognize many and
different environmental patterns and avoid obstacles in real time. Moreover, by
using proposed WNNstechnique mobile robot has successfully reached the goal in
dynamic environment compare to fuzzy logic technique and logic function,
capable of dealing with uncertainty in sensor reading, achieving good
performance in performing control actions with 0.56% error rate in mobile robot
speed.
Author: Siti Nurmaini, Bambang
Tutuko
Journal Code: jptkomputergg110053
