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Global Path Planning of Mobile Robot Using String and Modified SOFM  

Cha, Young-Youp (원광대학교 공과대학 기계자동차공학부)
Publication Information
Journal of the Korean Society for Precision Engineering / v.25, no.4, 2008 , pp. 69-76 More about this Journal
Abstract
The self-organizing feature map(SOFM) among a number of neural network uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 1-dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the method using string and the modified neural network is useful tool to mobile robot for the global path planning.
Keywords
Global path planning; Mobile robot;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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