Neural network methods for the localization and navigation of mobile robots

Zeng Guang Hou, Min Tan, Madan M. Gupta, Peter N. Nikiforuk, Noriyasu Homma

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

Neural networks, including support vector machines (SVMs), and principal component analysis (PCA) etc., and their applications have received increasing attentions from many fields such as information processing and systems control. In this paper, we study some useful features of the neural networks, support vector machines and principal component analysis, and apply these methods to the information processing, localization and navigation problems of mobile robots on the basis of the information acquired with multiple sensors, such as visual, ultrasonic and infrared sensors. A Hopfield-type neural network scheme is proposed for real-time optimization and navigation applications. To recognize the doorplate numbers and human faces, support vector machine is proposed for the vision system of the mobile robot. The principal component analysis network is used to process the data acquired by the ultrasonic and infrared sensors to obtain the location and orientation information of the robot. The principal component network can also give feasible directions for movements.

Original languageEnglish
Article number1557158
Pages (from-to)1057-1060
Number of pages4
JournalCanadian Conference on Electrical and Computer Engineering
Volume2005
DOIs
Publication statusPublished - 2005 Dec 1
EventCanadian Conference on Electrical and Computer Engineering 2005 - Saskatoon, SK, Canada
Duration: 2005 May 12005 May 4

Keywords

  • Information fusion
  • Localization
  • Mobile robots
  • Navigation
  • Neural networks, support vector machines (SVM)
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Neural network methods for the localization and navigation of mobile robots'. Together they form a unique fingerprint.

Cite this