Comparative study of Genetic Algorithm and Ant Colony Optimization algorithm performances for robot path planning in global static environments of different complexities

This paper presents the application of genetic algorithm (ga) and ant colony optimization (ACO) algorithm for robot path planning (RPP) in global static environment. Both algorithms were applied within global maps that consist of different number of free space nodes. These nodes generally represent...

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Bibliographic Details
Published in2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation pp. 132 - 137
Main Authors Sariff, N.B., Buniyamin, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Ant colony optimization
Artificial intelligence
Artificial neural networks
Genetic algorithms
Heuristic algorithms
Intelligent agent
Mobile robots
Orbital robotics
Path planning
Performance evaluation
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ISBN1424448085
9781424448081
DOI10.1109/CIRA.2009.5423220

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Summary:This paper presents the application of genetic algorithm (ga) and ant colony optimization (ACO) algorithm for robot path planning (RPP) in global static environment. Both algorithms were applied within global maps that consist of different number of free space nodes. These nodes generally represent the free space extracted from the robot map. Performances between both algorithms were compared and evaluated in terms of speed and number of iterations that each algorithm takes to find an optimal path within several selected environments. The effectiveness and efficiency of both algorithms were tested using a simulation approach. Comparison of the performances and parameter settings, advantages and limitations of both algorithms presented herewith can be used to further expand the optimization algorithm in RPP research area.
ISBN:1424448085
9781424448081
DOI:10.1109/CIRA.2009.5423220