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Navigation of mobile robot using location map of place cells and reinforcement learning.

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Title: Navigation of mobile robot using location map of place cells and reinforcement learning.
Authors: Tanaka, Toshio1, Nishida, Kenji1, Kurita, Takio1
Source: Systems & Computers in Japan. 6/30/2007, Vol. 38 Issue 7, p65-75. 11p. 1 Black and White Photograph, 3 Diagrams, 8 Graphs.
Subjects: Mobile robots, Cybernetic art, Technology, Computer science, Cells
Abstract: It is known that the hippocampus of rats contains neural cells called “place cells.” Place cells are neural cells that respond selectively when the rat arrives at a particular place. This paper proposes a mobile robot navigation method that uses a place cell position map and reinforcement learning. First, the place cell position map is created by a neural gas using image data and position data from observation points. Next, routes between the place cells are established, and the path to the goal is learned using the actor– critic method, which is a type of reinforcement learning method. Numerical simulations demonstrate that the goal can be reached even if there are motion errors by moving the robot along the route. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(7): 65– 75, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20632 [ABSTRACT FROM AUTHOR]
Copyright of Systems & Computers in Japan is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Navigation of mobile robot using location map of place cells and reinforcement learning.
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  Data: <searchLink fieldCode="JN" term="%22Systems+%26+Computers+in+Japan%22">Systems & Computers in Japan</searchLink>. 6/30/2007, Vol. 38 Issue 7, p65-75. 11p. 1 Black and White Photograph, 3 Diagrams, 8 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br /><searchLink fieldCode="DE" term="%22Cybernetic+art%22">Cybernetic art</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Cells%22">Cells</searchLink>
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  Data: It is known that the hippocampus of rats contains neural cells called “place cells.” Place cells are neural cells that respond selectively when the rat arrives at a particular place. This paper proposes a mobile robot navigation method that uses a place cell position map and reinforcement learning. First, the place cell position map is created by a neural gas using image data and position data from observation points. Next, routes between the place cells are established, and the path to the goal is learned using the actor– critic method, which is a type of reinforcement learning method. Numerical simulations demonstrate that the goal can be reached even if there are motion errors by moving the robot along the route. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(7): 65– 75, 2007; Published online in Wiley InterScience (<URL>www.interscience.wiley.com</URL>). DOI 10.1002/scj.20632 [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Systems & Computers in Japan is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 6/30/2007
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