Self-Localization of Guide Robots Through Image Classification
The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study require...
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2024-02-01
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| Series: | Baghdad Science Journal |
| Subjects: | |
| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9648 |
| _version_ | 1797292112518053888 |
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| author | Muhammad S. Alam Farhan B. Mohamed AKM B. Hossain |
| author_facet | Muhammad S. Alam Farhan B. Mohamed AKM B. Hossain |
| author_sort | Muhammad S. Alam |
| collection | DOAJ |
| description |
The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accurate solution for indoor robot navigation. The more accurate solution of the guide robotic system opens a new window of the self-localization system and solves the more complex problem of indoor robot navigation. It makes a reliable interface between humans and robots. This study successfully demonstrated how a robot finds its initial position inside a room. A deep learning system, such as a convolutional neural network, trains the self-localization system as an image classification problem. The robot was placed inside the room to collect images using a panoramic camera. Two datasets were created from the room images based on the height above and below the chest. The above-mentioned method achieved a localization accuracy of 98.98%.
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| first_indexed | 2024-03-07T19:47:02Z |
| format | Article |
| id | doaj.art-144cbb1ca12949519aa18927fc3be158 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-03-07T19:47:02Z |
| publishDate | 2024-02-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-144cbb1ca12949519aa18927fc3be1582024-02-28T20:06:30ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862024-02-01212(SI)10.21123/bsj.2024.9648Self-Localization of Guide Robots Through Image ClassificationMuhammad S. Alam0Farhan B. Mohamed1AKM B. Hossain2Department of Emergent Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia & Department of Computer Science, University of Bisha, Bisha, Saudi Arabia.Department of Emergent Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia & Department of Media and Game Innovation, Centre of Excellence (MaGICX), Universiti Teknologi Malaysia, Johor Bahru, Malaysia.Department of Emergent Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia & Department of Information Systems, University of Bisha, Bisha, Saudi Arabia. The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accurate solution for indoor robot navigation. The more accurate solution of the guide robotic system opens a new window of the self-localization system and solves the more complex problem of indoor robot navigation. It makes a reliable interface between humans and robots. This study successfully demonstrated how a robot finds its initial position inside a room. A deep learning system, such as a convolutional neural network, trains the self-localization system as an image classification problem. The robot was placed inside the room to collect images using a panoramic camera. Two datasets were created from the room images based on the height above and below the chest. The above-mentioned method achieved a localization accuracy of 98.98%. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9648Convolutional Neural Network, Deep Learning, Guide Robot, Image Classification, Self- localization |
| spellingShingle | Muhammad S. Alam Farhan B. Mohamed AKM B. Hossain Self-Localization of Guide Robots Through Image Classification Baghdad Science Journal Convolutional Neural Network, Deep Learning, Guide Robot, Image Classification, Self- localization |
| title | Self-Localization of Guide Robots Through Image Classification |
| title_full | Self-Localization of Guide Robots Through Image Classification |
| title_fullStr | Self-Localization of Guide Robots Through Image Classification |
| title_full_unstemmed | Self-Localization of Guide Robots Through Image Classification |
| title_short | Self-Localization of Guide Robots Through Image Classification |
| title_sort | self localization of guide robots through image classification |
| topic | Convolutional Neural Network, Deep Learning, Guide Robot, Image Classification, Self- localization |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9648 |
| work_keys_str_mv | AT muhammadsalam selflocalizationofguiderobotsthroughimageclassification AT farhanbmohamed selflocalizationofguiderobotsthroughimageclassification AT akmbhossain selflocalizationofguiderobotsthroughimageclassification |