NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

Thanh-Lam BUI


Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, (Viet Nam)

Ngoc-Tien TRAN

tientn@haui.edu.vn
Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, (Viet Nam)
https://orcid.org/0000-0001-5099-3758

Abstract

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. 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. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation


Keywords:

mobile robot, navigation, deep learning, computer vision

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Published
2023-06-30

Cited by

BUI, T.-L., & TRAN, N.-T. (2023). NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT. Applied Computer Science, 19(2), 82–95. https://doi.org/10.35784/acs-2023-16

Authors

Thanh-Lam BUI 

Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Viet Nam

Authors

Ngoc-Tien TRAN 
tientn@haui.edu.vn
Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Viet Nam
https://orcid.org/0000-0001-5099-3758

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