Global path planning of mobile robots using a memetic algorithm

Zexuan Zhu, Fangxiao Wang, Shan He, Yiwen Sun

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.

Original languageEnglish
Pages (from-to)1982-1993
Number of pages12
JournalInternational Journal of Systems Science
Volume46
Issue number11
Early online date9 Oct 2013
DOIs
Publication statusPublished - 18 Aug 2015

Keywords

  • evolutionary algorithm
  • genetic algorithm
  • global path planning
  • memetic algorithm
  • mobile robot

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications

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