Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation

The aim of this study was to implement and compare obstacle avoidance for an autonomous wheeled mobile robot (WMR) via the grey wolf optimisation (GWO) algorithm and the artificial bee colony (ABC) algorithm. The study was conducted via three scenarios, each designed to test the performance of the...

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Main Authors: Zahraa Abdulla, Nabil Hadi
Format: Article
Language:English
Published: Al-Khwarizmi College of Engineering – University of Baghdad 2025-06-01
Series:Al-Khawarizmi Engineering Journal
Subjects:
Online Access:https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/953
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author Zahraa Abdulla
Nabil Hadi
author_facet Zahraa Abdulla
Nabil Hadi
author_sort Zahraa Abdulla
collection DOAJ
description The aim of this study was to implement and compare obstacle avoidance for an autonomous wheeled mobile robot (WMR) via the grey wolf optimisation (GWO) algorithm and the artificial bee colony (ABC) algorithm. The study was conducted via three scenarios, each designed to test the performance of the algorithm under different conditions, considering fixed and moving circular obstacles in the surrounding environment. GWO was used to determine the most efficient, shortest and safest path for the WMR from the starting point to the target point. The results showed that the GWO outperformed the ABC. The GWO also enabled the WMR to avoid obstacles faster by 11.8%, 2.8% and 4.6% and with distances shorter by 1.42%, 2.2% and 1.97% for the three scenarios, respectively.
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publishDate 2025-06-01
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spelling doaj-art-00f1c6da74fc4a8d888035c67a41817e2025-08-20T03:07:43ZengAl-Khwarizmi College of Engineering – University of BaghdadAl-Khawarizmi Engineering Journal1818-11712312-07892025-06-0121210.22153/kej.2025.09.003Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf OptimisationZahraa Abdulla0Nabil Hadi1Department of Mechanical Engineering, College of Engineering, University of Baghdad, Baghdad, IraqDepartment of Aeronautical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq The aim of this study was to implement and compare obstacle avoidance for an autonomous wheeled mobile robot (WMR) via the grey wolf optimisation (GWO) algorithm and the artificial bee colony (ABC) algorithm. The study was conducted via three scenarios, each designed to test the performance of the algorithm under different conditions, considering fixed and moving circular obstacles in the surrounding environment. GWO was used to determine the most efficient, shortest and safest path for the WMR from the starting point to the target point. The results showed that the GWO outperformed the ABC. The GWO also enabled the WMR to avoid obstacles faster by 11.8%, 2.8% and 4.6% and with distances shorter by 1.42%, 2.2% and 1.97% for the three scenarios, respectively. https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/953Bees Algorithm; Grey Wolf Optimisation; Wheeled Mobile Robot.
spellingShingle Zahraa Abdulla
Nabil Hadi
Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
Al-Khawarizmi Engineering Journal
Bees Algorithm; Grey Wolf Optimisation; Wheeled Mobile Robot.
title Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
title_full Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
title_fullStr Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
title_full_unstemmed Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
title_short Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
title_sort autonomous path planning and obstacle avoidance of a wheeled mobile robot via grey wolf optimisation
topic Bees Algorithm; Grey Wolf Optimisation; Wheeled Mobile Robot.
url https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/953
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