Presentation 2008-03-13
Graph-based Maps Formation for Mobile Robots by Hidden Markov Models
Muhammad Aziz Muslim, Masumi ISHIKAWA,
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Abstract(in English) The present paper proposes a probabilistic approach to recognizing the environment of a mobile robot and to generate a graph-based map based on the estimation of Hidden Markov Models (HMMs). This is because recognition of the environment based on a short interval of data is not enough when sensory signals are corrupted by noise. Graph-based maps are effective in decreasing the computational cost. Two methods for constructing graph-based maps are proposed. The former is to estimate HMMs based on quantized sensory-motor signals. The latter is to estimate HMMs based on a sequence of labels obtained by modular network SOM (mnSOM). The resulting sequence of HMMs can be converted into a graph-based map in a straightforward way. Simulation results demonstrate that the proposed method is able to construct graph-based maps effectively, and to perform goal seeking efficiently.
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Keyword(in English) Hidden Markov Model / Graph-based Map / mnSOM / k-means / mobile robot
Paper # NC2007-164
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Committee NC
Conference Date 2008/3/5(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Graph-based Maps Formation for Mobile Robots by Hidden Markov Models
Sub Title (in English)
Keyword(1) Hidden Markov Model
Keyword(2) Graph-based Map
Keyword(3) mnSOM
Keyword(4) k-means
Keyword(5) mobile robot
1st Author's Name Muhammad Aziz Muslim
1st Author's Affiliation Department of Brain Science and Engineering, Kyushu Institute of Technology()
2nd Author's Name Masumi ISHIKAWA
2nd Author's Affiliation Department of Brain Science and Engineering, Kyushu Institute of Technology
Date 2008-03-13
Paper # NC2007-164
Volume (vol) vol.107
Number (no) 542
Page pp.pp.-
#Pages 6
Date of Issue