Motion Recognition System for Mobile Robot in Logistics Center
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초록
Motion recognition systems are crucial for mobile robots, particularly in logistics centers, where they need to interact with other robots or workers. Mobile robots require the capability to recognize movement patterns of surrounding objects to avoid collisions and maintain safety. Employing computer vision, machine learning, and deep learning techniques, cameras have become powerful sensors for object recognition and tracking. In this research, the YOLOv8 (You Only Look Once) model is utilized to detect and track object movements. The training data were gathered from videos of people and boxes in a lab setting. The system operates in real-time to meet the collision avoidance needs of mobile robots. The data captured by the camera is processed to analyze detected objects' movement patterns. The results demonstrate the success in real-time motion recognition and the capability of providing safety alerts when a tracked object enters the robot's safety perimeter.
Abstract
Motion recognition systems are crucial for mobile robots, particularly in logistics centers, where they need to interact with other robots or workers. Mobile robots require the capability to recognize movement patterns of surrounding objects to avoid collisions and maintain safety. Employing computer vision, machine learning, and deep learning techniques, cameras have become powerful sensors for object recognition and tracking. In this research, the YOLOv8 (You Only Look Once) model is utilized to detect and track object movements. The training data were gathered from videos of people and boxes in a lab setting. The system operates in real-time to meet the collision avoidance needs of mobile robots. The data captured by the camera is processed to analyze detected objects' movement patterns. The results demonstrate the success in real-time motion recognition and the capability of providing safety alerts when a tracked object enters the robot's safety perimeter.
참고문헌 (15)
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