Navigation strategy for mobile robot based on computer vision and YOLOv5 network in the unknown environment

• Autorzy: Bui Thanh-Lam , Tran Ngoc-Tien

Identyfikatory

DOI

10.35784/acs-2023-16

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

mobile robot; navigation; deep learning; computer vision

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 2
• Strony: 82--95
• Opis fizyczny: Bibliogr. 15 poz., fig., tab.

Twórcy

autor

Bui Thanh-Lam

• Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Vietnam

• https://orcid.org/0000-0003-2859-6670

autor

Tran Ngoc-Tien

• Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Vietnam

• https://orcid.org/0000-0001-5099-3758

Bibliografia

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