Optimal path planning for autonomous mobile robot navigation using ant colony optimization and a fuzzy cost function evaluation

M. A. Porta García, Oscar Montiel, Oscar Castillo, Roberto Sepúlveda

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

17 Scopus citations

Abstract

In this work, a method for finding the optimal path from an initial point to a final one in a previously defined static search map is presented, based on Ant Colony Optimization Meta-Heuristic (ACO-MH). The proposed algorithm supports the avoidance of dynamic obstacles; that is, once the optimal path is found and the robot starts navigating, if the robot's route is interrupted by a new obstacle that was sensed at time t, it will recalculate an alternative optimal path from the actual robot position in order to surround this blocking object and reach the goal.

Original languageEnglish
Title of host publicationAnalysis and Design of Intelligent Systems using Soft Computing Techniques
EditorsPatricia Melin, Eduardo Gomez Ramirez, Janusz Kacprzyk, Witold Pedrycz
Pages790-798
Number of pages9
DOIs
StatePublished - 2007
Externally publishedYes

Publication series

NameAdvances in Soft Computing
Volume41
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

Keywords

  • Ant Colony Optimization
  • Autonomous Mobile Robot Navigation
  • Fuzzy logic
  • Path planning

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