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Author:

Gao, Yuan-Yuan (Gao, Yuan-Yuan.) | Ruan, Xiao-Gang (Ruan, Xiao-Gang.) | Li, Jian-Geng (Li, Jian-Geng.) | Song, Hong-Jun (Song, Hong-Jun.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

Aiming at the problems in unknown environment for mobile robots path planning, a dynamic growing self-organizing map with A* (DGSOM_A*) algorithm was proposed and used in the map building and path planning. Firstly, the method used a two-dimentional simulation software Mobotsim to obtain environmental information samples for DGSOM_A* by moving the robot without collision, and then built the environment map through continuously increasing the new SOM neurons with the robot movement, thus generated the topology map to describe the environment information with a few SOM neurons. Finally, it completed navigation by finding an optimal path from the start point to the target. The experimental results show that the proposed DGSOM_A* method detects the complex desktop environment effectively and correctly and the robot can realize environment mapping automatically by comparing to the SOM algorithm.

Keyword:

Mobile robots Robot programming Motion planning Conformal mapping Self organizing maps Computer software

Author Community:

  • [ 1 ] [Gao, Yuan-Yuan]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Gao, Yuan-Yuan]College of Information and Engineering, Zhejiang A and F University, Hangzhou 311300, China
  • [ 3 ] [Ruan, Xiao-Gang]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Li, Jian-Geng]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 5 ] [Song, Hong-Jun]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2012

Issue: 12

Volume: 38

Page: 1862-1867

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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