NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT
The capacity to navigate effectively in complex environments is a crucial prerequisite for mobile robots. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insuf...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2023-06-01
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| Series: | Applied Computer Science |
| Subjects: | |
| Online Access: | http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=567:navigation-strategy-for-mobile-robot-based-on-computer-vision-and-yolov5-network-in-the-unknown-environment&catid=97:vol-19-no-22023&Itemid=171 |
| _version_ | 1797782635069571072 |
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| author | Thanh-Lam BUI Ngoc-Tien TRAN |
| author_facet | Thanh-Lam BUI Ngoc-Tien TRAN |
| author_sort | Thanh-Lam BUI |
| collection | DOAJ |
| description | The capacity to navigate effectively in complex environments is a crucial prerequisite for mobile robots. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. When the light illumination increases from 300 lux to 1000 lux, the reliability of the recognition model on different objects also improves, from about 75% to 98%, respectively. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation. |
| first_indexed | 2024-03-13T00:13:44Z |
| format | Article |
| id | doaj.art-9c06b903498b40819b47e61447c080e7 |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-03-13T00:13:44Z |
| publishDate | 2023-06-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-9c06b903498b40819b47e61447c080e72023-07-12T05:39:33ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772023-06-01192829510.35784/acs-2023-16NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENTThanh-Lam BUI 0https://orcid.org/0000-0003-2859-6670Ngoc-Tien TRAN 1https://orcid.org/0000-0001-5099-3758 Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Vietnam, tientn@haui.edu.vnHanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Vietnam, tientn@haui.edu.vnThe capacity to navigate effectively in complex environments is a crucial prerequisite for mobile robots. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. When the light illumination increases from 300 lux to 1000 lux, the reliability of the recognition model on different objects also improves, from about 75% to 98%, respectively. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation.http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=567:navigation-strategy-for-mobile-robot-based-on-computer-vision-and-yolov5-network-in-the-unknown-environment&catid=97:vol-19-no-22023&Itemid=171mobile robotnavigationdeep learningcomputer vision |
| spellingShingle | Thanh-Lam BUI Ngoc-Tien TRAN NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT Applied Computer Science mobile robot navigation deep learning computer vision |
| title | NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT |
| title_full | NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT |
| title_fullStr | NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT |
| title_full_unstemmed | NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT |
| title_short | NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT |
| title_sort | navigation strategy for mobile robot based on computer vision and yolov5 network in the unknown environment |
| topic | mobile robot navigation deep learning computer vision |
| url | http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=567:navigation-strategy-for-mobile-robot-based-on-computer-vision-and-yolov5-network-in-the-unknown-environment&catid=97:vol-19-no-22023&Itemid=171 |
| work_keys_str_mv | AT thanhlambui navigationstrategyformobilerobotbasedoncomputervisionandyolov5networkintheunknownenvironment AT ngoctientran navigationstrategyformobilerobotbasedoncomputervisionandyolov5networkintheunknownenvironment |