AN AUTONOMOUS MOBILE ROBOT SOLUTION FOR PATROLLING  AND INTRUDER DETECTION USING YOLOv9

Nguyễn Hữu Cường; Lưu Trọng Hiếu; Ngô Quang Hiếu

doi:10.34238/tnu-jst.12443

AN AUTONOMOUS MOBILE ROBOT SOLUTION FOR PATROLLING AND INTRUDER DETECTION USING YOLOv9

About this article

Received: 01/04/25 Revised: 14/07/25 Published: 14/07/25

Authors

1. Nguyen Huu Cuong, College of Engineering - Can Tho University
2. Luu Trong Hieu , College of Engineering - Can Tho University
3. Ngo Quang Hieu, College of Engineering - Can Tho University

Abstract

This study proposes a solution using a mobile robot for patrolling and detecting intruders in an indoor environment based on the YOLOv9 deep learning model. The robot is designed with a two-wheeled differential drive mechanism and integrated with sensors, including a LiDAR for mapping and an Intel D453i camera for human detection. A PID controller is implemented to ensure stable operation throughout the process. After the SLAM-based mapping phase, the robot performs patrols using the Behavior Tree method on the ROS2 platform. The YOLOv9 deep learning model enables the robot to detect intruders, both with and without masks, in a maze environment. Experimental results show that the robot successfully detects all individuals in the maze after two patrol rounds, functioning effectively in both day and night conditions. These findings highlight the practical potential of mobile robots in patrolling and alarm systems.

Keywords

Mobile robot; YOLOv9; Patrolling; Human detection; Behavior tree

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DOI: https://doi.org/10.34238/tnu-jst.12443

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