Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin
This paper presents the application of Genetic Algorithm 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 f...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
UiTM Press
2010
|
| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/61874/1/61874.pdf https://ir.uitm.edu.my/id/eprint/61874/ https://jeesr.uitm.edu.my/v1/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839753464426528768 |
|---|---|
| author | Sariff, Nohaidda Buniyamin, Norlida |
| author_facet | Sariff, Nohaidda Buniyamin, Norlida |
| author_sort | Sariff, Nohaidda |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper presents the application of Genetic Algorithm 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. |
| format | Article |
| id | my.uitm.ir-61874 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2010 |
| publisher | UiTM Press |
| record_format | eprints |
| spelling | my.uitm.ir-618742025-07-31T03:33:32Z https://ir.uitm.edu.my/id/eprint/61874/ Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin jeesr Sariff, Nohaidda Buniyamin, Norlida Evolutionary programming (Computer science). Genetic algorithms This paper presents the application of Genetic Algorithm 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. UiTM Press 2010-06 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/61874/1/61874.pdf Sariff, Nohaidda and Buniyamin, Norlida (2010) Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin. (2010) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29.html>, 3 (1): 1. pp. 1-11. ISSN 1985-5389 https://jeesr.uitm.edu.my/v1/ |
| spellingShingle | Evolutionary programming (Computer science). Genetic algorithms Sariff, Nohaidda Buniyamin, Norlida Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin |
| title | Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin |
| title_full | Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin |
| title_fullStr | Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin |
| title_full_unstemmed | Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin |
| title_short | Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin |
| title_sort | evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / nohaidda sariff and norlida buniyamin |
| topic | Evolutionary programming (Computer science). Genetic algorithms |
| url | https://ir.uitm.edu.my/id/eprint/61874/1/61874.pdf https://ir.uitm.edu.my/id/eprint/61874/ https://jeesr.uitm.edu.my/v1/ |
| url_provider | http://ir.uitm.edu.my/ |