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World Modeling in Disaster Environments with Constructive Self-Organizing Maps for Autonomous Search and Rescue Robots

Çetin Meriçli, I. Osman Tufanoğullari, and H. Levent Akın. World Modeling in Disaster Environments with Constructive Self-Organizing Maps for Autonomous Search and Rescue Robots. In RoboCup, pp. 467–473, 2004.

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Abstract

This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world modeling for search and rescue operations in disaster environments. In our approach, nodes of the self organizing network consist of victim and waypoint classes where victim denotes a human being waiting to be rescued and waypoint denotes a free space that can be reached from the entrance of debris. The proposed approach performed better than traditional self-organizing maps in terms of both the accuracy of the output and the learning speed. In this paper the detailed explanation of the approach and some experimental results are given.

BibTeX

@inproceedings{DBLP-conf-robocup-MericliTA04,
  author    = {Çetin Meriçli and I. Osman Tufanoğullari and H. Levent Akın},
  title     = {World Modeling in Disaster Environments with Constructive
               Self-Organizing Maps for Autonomous Search and Rescue Robots},
  booktitle = {RoboCup},
  year      = {2004},
  pages     = {467-473},
  ee        = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3276{\&}spage=467},
  crossref  = {DBLP:conf/robocup/2004},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  abstract  = {This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world modeling for search and rescue operations in disaster environments. In our approach, nodes of the self organizing network consist of victim and waypoint classes where victim denotes a human being waiting to be rescued and waypoint denotes a free space that can be reached from the entrance of debris. The proposed approach performed better than traditional self-organizing maps in terms of both the accuracy of the output and the learning speed. In this paper the detailed explanation of the approach and some experimental results are given.}
  bib2html_dl_html = "http://www.springerlink.com/index/M9H8AKRJRQ392P2M"
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {World Modeling},
  bib2html_dl_pdf = {../files/cmericliRoboCup2004WorldModeling.pdf},
}

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