Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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End date: 2009Subject: search.filters.subject.Mobile robots, Position estimation, Unsupervised learning

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    Position Estimation of Mobile Robots Using Unsupervised Learning Algorithms
    (Springer, Berlin, Heidelberg, 2009-09-28)
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    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.
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    Item type:Publication,
    Position Estimation of Mobile Robots Using Unsupervised Learning Algorithms
    (Springer, Berlin, Heidelberg, 2009-09-28)
    ;
    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.