Enhancing Robots Navigation in Internet of Things Indoor Systems

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima encl...

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Autores principales:	Yahya Tashtoush, Israa Haj-Mahmoud, Omar Darwish, Majdi Maabreh, Belal Alsinglawi, Mahmoud Elkhodr, Nasser Alsaedi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	local minima target switching trap situation mobile robot navigation infinite loop Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/f78bd42be9014e289445d947b44ad6ec
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id	oai:doaj.org-article:f78bd42be9014e289445d947b44ad6ec
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spelling	oai:doaj.org-article:f78bd42be9014e289445d947b44ad6ec2021-11-25T17:17:30ZEnhancing Robots Navigation in Internet of Things Indoor Systems10.3390/computers101101532073-431Xhttps://doaj.org/article/f78bd42be9014e289445d947b44ad6ec2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/153https://doaj.org/toc/2073-431XIn this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.Yahya TashtoushIsraa Haj-MahmoudOmar DarwishMajdi MaabrehBelal AlsinglawiMahmoud ElkhodrNasser AlsaediMDPI AGarticlelocal minimatarget switchingtrap situationmobile robot navigationinfinite loopElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 153, p 153 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	local minima target switching trap situation mobile robot navigation infinite loop Electronic computers. Computer science QA75.5-76.95
spellingShingle	local minima target switching trap situation mobile robot navigation infinite loop Electronic computers. Computer science QA75.5-76.95 Yahya Tashtoush Israa Haj-Mahmoud Omar Darwish Majdi Maabreh Belal Alsinglawi Mahmoud Elkhodr Nasser Alsaedi Enhancing Robots Navigation in Internet of Things Indoor Systems
description	In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.
format	article
author	Yahya Tashtoush Israa Haj-Mahmoud Omar Darwish Majdi Maabreh Belal Alsinglawi Mahmoud Elkhodr Nasser Alsaedi
author_facet	Yahya Tashtoush Israa Haj-Mahmoud Omar Darwish Majdi Maabreh Belal Alsinglawi Mahmoud Elkhodr Nasser Alsaedi
author_sort	Yahya Tashtoush
title	Enhancing Robots Navigation in Internet of Things Indoor Systems
title_short	Enhancing Robots Navigation in Internet of Things Indoor Systems
title_full	Enhancing Robots Navigation in Internet of Things Indoor Systems
title_fullStr	Enhancing Robots Navigation in Internet of Things Indoor Systems
title_full_unstemmed	Enhancing Robots Navigation in Internet of Things Indoor Systems
title_sort	enhancing robots navigation in internet of things indoor systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f78bd42be9014e289445d947b44ad6ec
work_keys_str_mv	AT yahyatashtoush enhancingrobotsnavigationininternetofthingsindoorsystems AT israahajmahmoud enhancingrobotsnavigationininternetofthingsindoorsystems AT omardarwish enhancingrobotsnavigationininternetofthingsindoorsystems AT majdimaabreh enhancingrobotsnavigationininternetofthingsindoorsystems AT belalalsinglawi enhancingrobotsnavigationininternetofthingsindoorsystems AT mahmoudelkhodr enhancingrobotsnavigationininternetofthingsindoorsystems AT nasseralsaedi enhancingrobotsnavigationininternetofthingsindoorsystems
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Enhancing Robots Navigation in Internet of Things Indoor Systems

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