Paper Info
Title
Self-Organization Of Spiking Neural Network Generating Autonomous Behavior In A Miniature Mobile Robot
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).
Year
DOI
Venue
2005
10.1007/3-540-29344-2_38
PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON AUTONOMOUS MINIROBOTS FOR RESEARCH AND EDUTAINMENT (AMIRE 2005)
Keywords
Field
DocType
mobile robot,spiking neural network,self organization
Obstacle avoidance,Genetic Evolution,Autonomous behavior,Computer science,Self-organization,Artificial intelligence,Robot,Spiking neural network,Mobile robot,Genetic algorithm
Conference
Citations 
PageRank 
References 
2
0.37
3
Authors
2
Name
Order
Citations
PageRank
Fady Alnajjar16612.23
Kazuyuki Murase2103875.66