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

Xiaoming Kang, Yong Yue*, Dayou Li, Carsten Maple

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)177-184
Number of pages8
JournalInternational Journal of Computer Applications in Technology
Issue number3-4
Publication statusPublished - Sept 2011
Externally publishedYes


  • Dead end
  • Genetic algorithm
  • Obstacle avoidance
  • Robot navigation

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