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Genetic algorithm based solution to dead-end problems in robot navigation

  • Xiaoming Kang
    ,
  • Yong Yue
    ,
  • Dayou Li
    ,
  • Carsten Maple
Research Output: Contribution to journal Article Peer-review

Abstract

In robot navigation, mobile robots can suffer from dead-end problems, that is, they can be stuck in areas which are surrounded by obstacles. Attempts have been reported to avoid a robot entering into such a dead-end area. However, in some applications, for example, rescue work, the dead-end areas must be explored. Therefore, it is vital for the robot to come out from the dead-end areas after exploration. This paper presents an approach which enables a robot to come out from dead-end areas. There are two main parts: a dead-end detection mechanism and a genetic algorithm (GA) based online training mechanism. When the robot realises that it is stuck in a dead-end area, it will operate the online training to produce a new best chromosome that will enable the robot to escape from the area.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 177-184

Journal (Volume, Issue Number)

International Journal of Computer Applications in Technology (Volume 41, Issue 3/4)

Publication milestones

  • Published - 01/01/2011

Publication status

Published - 01/01/2011

ISSN

0952-8091

External Publication IDs

  • handle.net: 10547/250938
  • Scopus: 80053375229

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