Embedded visual grasping system based on MaixCam and LAB color segmentation

Traditional embedded vision-based grasping systems often struggle with low recognition accuracy and poor adaptability to complex lighting conditions. To address these challenges, this study proposes an embedded visual grasping system based on MaixCam and LAB color segmentation, aiming to improve object recognition and grasping efficiency in embedded applications. The system utilizes LAB color space for precise object segmentation, enhancing recognition accuracy under varying lighting conditions. A lightweight algorithm is implemented on the MaixCam platform to achieve real-time processing, ensuring efficient object localization and grasping. Experimental results demonstrate the system's effectiveness in automated grasping tasks, highlighting its potential for embedded robotic applications.