Doorplate Recognition for a Mobile Robot Based on PSO and RBF Neural Network

Author

Zuo Guoyu ; Fan Yanfeng ; Qiao Junfei

Author_Institution

Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China

Volume

2

fYear

2010

fDate

13-14 March 2010

Firstpage

30

Lastpage

33

Abstract

This paper proposes a mixed optimization algorithm based on RBF neural network (RBF) and Particle Swarm Optimization (PSO), which is applied to the doorplate recognition for a mobile robot. The centers and widths of RBF neural network are determined with self-increasing clustering algorithm, and the improved particle swarm optimization algorithm is used to optimize their distance from the threshold. Experimental results show that this algorithm has an advantage over traditional neural network algorithm in terms of accuracy recognition ratio and convergence rate. Hence, the proposed algorithm can meet the needs of robot vision system.

Keywords

convergence; mobile robots; particle swarm optimisation; pattern clustering; radial basis function networks; robot vision; PSO; RBF neural network; accuracy recognition ratio; convergence rate; doorplate recognition; mixed optimization algorithm; mobile robot; particle swarm optimization; robot vision system; self-increasing clustering algorithm; Clustering algorithms; Function approximation; Mobile robots; Neural networks; Paper technology; Particle swarm optimization; Radial basis function networks; Robot vision systems; Robotics and automation; Signal processing algorithms; PSO algorithm; RBF neural network; doorplate recognition; mobile robot;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.752

Filename

5460021