Object Recognition System for the Spinbotics Robotic Arm

Abstract

This study focuses on the development of a visual system designed to facilitate object detection for the Spinbotics robotic arm in spatial environments. The primary objective is to enable accurate detection and classification of diverse objects, enhancing the arm's capability to grasp and manipulate items effectively. The system employs the YOLOv7 deep neural network, fine-tuned using transfer learning on a local computing infrastructure. Compared to traditional methods like R-CNN and SSD, YOLOv7 offers superior real-time processing capabilities and efficiency, making it well-suited for dynamic environments. Through extensive training and testing, the system demonstrates robust performance in detecting objects across varied scenes and identifying optimal grasp points. This research underscores the effectiveness of integrating advanced computer vision techniques to enhance the operational efficiency and versatility of robotic manipulators in real-world applications.