Position Estimation of Mobile Robots Using Unsupervised Learning Algorithms
Date Issued
2009-09-28
Author(s)
Abstract
Estimating the position of a mobile robot in an environment is a
crucial issue. It allows the robot to obtain more precisely the knowledge of its
current state and to make the problem of generating command sequences for
achieving a certain goal an easier task. The robot learns the environment using
an unsupervised learning method and generates a percept – action- percept
graph, based on the readings of an ultrasound sensor. The graph is then used in
the process of position estimation by matching the current sensory reading
category with an existing node category. Our approach allows the robot to
generate a set of controls to reach a desired destination. For the learning of the
environment, two unsupervised algorithms FuzzyART neural network and
GNG network were used. The approach was tested for its ability to recognize
previously learnt positions. Both algorithms that were used were compared for
their precision.
crucial issue. It allows the robot to obtain more precisely the knowledge of its
current state and to make the problem of generating command sequences for
achieving a certain goal an easier task. The robot learns the environment using
an unsupervised learning method and generates a percept – action- percept
graph, based on the readings of an ultrasound sensor. The graph is then used in
the process of position estimation by matching the current sensory reading
category with an existing node category. Our approach allows the robot to
generate a set of controls to reach a desired destination. For the learning of the
environment, two unsupervised algorithms FuzzyART neural network and
GNG network were used. The approach was tested for its ability to recognize
previously learnt positions. Both algorithms that were used were compared for
their precision.
Subjects
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