Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms
This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Faculty of Engineering and Technology
2020-10-01
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| Series: | Nigerian Journal of Technological Development |
| Subjects: | |
| Online Access: | https://journal.njtd.com.ng/index.php/njtd/article/view/492 |
| _version_ | 1818008859983216640 |
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| author | Chika Yinka-Banjo U. Agwogie |
| author_facet | Chika Yinka-Banjo U. Agwogie |
| author_sort | Chika Yinka-Banjo |
| collection | DOAJ |
| description | This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). |
| first_indexed | 2024-04-14T05:34:40Z |
| format | Article |
| id | doaj.art-50fe50b59d654b71a55a396780afa6ca |
| institution | Directory Open Access Journal |
| issn | 2437-2110 |
| language | English |
| last_indexed | 2024-04-14T05:34:40Z |
| publishDate | 2020-10-01 |
| publisher | Faculty of Engineering and Technology |
| record_format | Article |
| series | Nigerian Journal of Technological Development |
| spelling | doaj.art-50fe50b59d654b71a55a396780afa6ca2022-12-22T02:09:41ZengFaculty of Engineering and TechnologyNigerian Journal of Technological Development2437-21102020-10-01173165173373Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization AlgorithmsChika Yinka-Banjo0U. AgwogieUniversity of LagosThis article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs).https://journal.njtd.com.ng/index.php/njtd/article/view/492swarm intelligencefruit fly algorithmant colony optimizationparticule swarm optimizationoptimal pathmobile robot |
| spellingShingle | Chika Yinka-Banjo U. Agwogie Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms Nigerian Journal of Technological Development swarm intelligence fruit fly algorithm ant colony optimization particule swarm optimization optimal path mobile robot |
| title | Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms |
| title_full | Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms |
| title_fullStr | Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms |
| title_full_unstemmed | Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms |
| title_short | Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms |
| title_sort | mobile robot path planning in an obstacle free static environment using multiple optimization algorithms |
| topic | swarm intelligence fruit fly algorithm ant colony optimization particule swarm optimization optimal path mobile robot |
| url | https://journal.njtd.com.ng/index.php/njtd/article/view/492 |
| work_keys_str_mv | AT chikayinkabanjo mobilerobotpathplanninginanobstaclefreestaticenvironmentusingmultipleoptimizationalgorithms AT uagwogie mobilerobotpathplanninginanobstaclefreestaticenvironmentusingmultipleoptimizationalgorithms |