Solving robot path planning problem using Ant Colony Optimisation (ACO) approach / Nordin Abu Bakar and Rosnawati Abdul Kudus

Abu Bakar, Nordin and Abdul Kudus, Rosnawati (2009) Solving robot path planning problem using Ant Colony Optimisation (ACO) approach / Nordin Abu Bakar and Rosnawati Abdul Kudus. Scientific Research Journal, 6 (1). pp. 65-76. ISSN 1675-7009

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Official URL: https://srj.uitm.edu.my/

Abstract

Learning is a complex cognitive process; thus, the algorithms that can simulate learning are also complex. The complexity is due to the fact that little is known about the learning process that can be simulated in a machine. In this study two methods have been chosen to navigate a simulated robot to a target point; namely, Ants Colony Optimisation (ACO) and the Fuzzy Approach. The focus
of this paper is primarily the ACO method and the Fuzzy Approach is used as a comparative benchmark. Three scenarios were designed: the Big Hall, the Wall Following and the Volcano Challenge. These experimental scenarios
represent the respective navigation frameworks found in the literature used to test learning algorithms. The results indicate that the ACO’s performance is inferior to the Fuzzy approach; justification for this has been discussed in
relation to previous research in this area. Some future work to investigate this phenomenon further and improve the performance of the ACO algorithm is also presented.

Metadata

Item Type: Article
Creators:
Creators
Email
Abu Bakar, Nordin
nordin@fskm.uitm.edu.my
Abdul Kudus, Rosnawati
UNSPECIFIED
Subjects: Q Science > Q Science (General) > Machine learning
Q Science > QA Mathematics > Fuzzy arithmetic
T Technology > TJ Mechanical engineering and machinery > Robotics. Robots. Manipulators (Mechanism) > Computer simulation
Divisions: Universiti Teknologi MARA, Shah Alam > Research Management Centre (RMC)
Journal or Publication Title: Scientific Research Journal
UiTM Journal Collections: UiTM Journal > Scientific Research Journal (SRJ)
ISSN: 1675-7009
Volume: 6
Number: 1
Page Range: pp. 65-76
Official URL: https://srj.uitm.edu.my/
Item ID: 12917
Uncontrolled Keywords: ant colony optimisation, fuzzy approach, machine learning, robot navigation
URI: https://ir.uitm.edu.my/id/eprint/12917

ID Number

12917

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