Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study

Alotaibi, Albandari; Alatawi, Hanan; Binnouh, Aseel; Duwayriat, Lamaa; Alhmiedat, Tareq; Alia, Osama Moh’d

doi:10.3390/technologies12090157

Open AccessArticle

Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study

by

Albandari Alotaibi

¹,

Hanan Alatawi

¹,

Aseel Binnouh

¹,

Lamaa Duwayriat

¹,

Tareq Alhmiedat

^2,3,*

and

Osama Moh’d Alia

¹

Computer Science Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47713, Saudi Arabia

²

Information Technology Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47713, Saudi Arabia

³

Artificial Intelligence and Sensing Technologies (AIST) Reseach Center, University of Tabuk, Tabuk 71491, Saudi Arabia

^*

Author to whom correspondence should be addressed.

Technologies 2024, 12(9), 157; https://doi.org/10.3390/technologies12090157

Submission received: 18 July 2024 / Revised: 2 September 2024 / Accepted: 6 September 2024 / Published: 10 September 2024

(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)

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Abstract

Robot semantic navigation has received significant attention recently, as it aims to achieve reliable mapping and navigation accuracy. Object detection tasks are vital in this endeavor, as a mobile robot needs to detect and recognize the objects in the area of interest to build an effective semantic map. To achieve this goal, this paper classifies and discusses recently developed object detection approaches and then presents the available vision datasets that can be employed in robot semantic navigation applications. In addition, this paper discusses several experimental studies that have validated the efficiency of object detection algorithms, including Faster R-CNN, YOLO v5, and YOLO v8. These studies also utilized a vision dataset to design and develop efficient robot semantic navigation systems, which is also discussed. According to several experiments conducted in a Fablab area, the YOLO v8 object classification model achieved the best results in terms of classification accuracy and processing speed.

Keywords: robot semantic navigation; deep learning-based vision systems; vision datasets

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MDPI and ACS Style

Alotaibi, A.; Alatawi, H.; Binnouh, A.; Duwayriat, L.; Alhmiedat, T.; Alia, O.M. Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study. Technologies 2024, 12, 157. https://doi.org/10.3390/technologies12090157

AMA Style

Alotaibi A, Alatawi H, Binnouh A, Duwayriat L, Alhmiedat T, Alia OM. Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study. Technologies. 2024; 12(9):157. https://doi.org/10.3390/technologies12090157

Chicago/Turabian Style

Alotaibi, Albandari, Hanan Alatawi, Aseel Binnouh, Lamaa Duwayriat, Tareq Alhmiedat, and Osama Moh’d Alia. 2024. "Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study" Technologies 12, no. 9: 157. https://doi.org/10.3390/technologies12090157

APA Style

Alotaibi, A., Alatawi, H., Binnouh, A., Duwayriat, L., Alhmiedat, T., & Alia, O. M. (2024). Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study. Technologies, 12(9), 157. https://doi.org/10.3390/technologies12090157

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study

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