Self-organization of spiking neural network generating autonomous behavior in a miniature mobile robot

Fady Alnajjar, K. Murase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Purpose of this study is to develop self-organization algorithm of spiking neural network applicable to autonomous robots. We first formulated a spiking neural network model whose inputs and outputs were analog. We then implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task exists with the spiking neural network, the robot was evolved with the genetic algorithm (GA) in an environment. The robot acquired the obstacle-avoidance and navigation task successfully, exhibiting the presence of the solution. Then, a self-organization algorithm based on the use-dependent synaptic potentiation and depotentiation was formulated and implemented into the robot. In the environment, the robot gradually organized the network and the obstacle avoidance behavior was formed. The time needed for the training was much less than with genetic evolution, approximately one fifth (1/5).

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment, AMiRE 2005
Pages255-260
Number of pages6
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event3rd International Symposium on Autonomous Minirobots for Research and Edutainment, AMiRE 2005 - Fukui, Japan
Duration: Sep 20 2005Sep 22 2005

Publication series

NameProceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment, AMiRE 2005

Conference

Conference3rd International Symposium on Autonomous Minirobots for Research and Edutainment, AMiRE 2005
Country/TerritoryJapan
CityFukui
Period9/20/059/22/05

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Education

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