Progress And Comprehensive Analysis of Intelligent Grasping Technology for Robotic Arms Based on Computer Vision

Abstract

With the continuous progress of technology and the increasing demand, industrial robotic arms have gradually developed into an important production tool. The current research goal for the development and application of industrial robotic arms is to improve production efficiency, reduce costs, improve quality and safety, in order to achieve industrial automation and intelligence. The application of computer vision technology can improve the autonomy, flexibility, and intelligence level of industrial robotic arms, enabling them to play a better role in industrial production. This article first introduces several different visual systems, including Eye-in-hand and Eye-to-hand (monocular, binocular vision systems, heterogeneous perception systems) for robotic arms. Then, three algorithms for feature extraction of robotic arm grasping targets were introduced and explained: SIFT, SURF, and ORB. Finally, summarize the analysis of workpiece target feature extraction and matching techniques applied to industrial robotic arms. This article summarizes several technical research directions for improving the performance of industrial robotic arms. It has reference significance for improving the production efficiency and quality of robotic arms, achieving automation and intelligence, adapting to complex environments, and expanding the application fields of robotic arms. In the future, intelligent recognition and grasping control of robotic arms may be further integrated with other technologies such as artificial intelligence, machine learning, sensor technology, etc., to enhance the intelligence level of the system. The robotic arm can adapt better to different tasks and environments through continuous learning and adaptive adjustment, improving its versatility and flexibility.