Research on grasping model based on visual recognition robot arm

Abstract

This article mainly systematically describes the research based on the visual recognition robotic arm. With the advancement of science and technology, the robot industry has also seen significant improvement in recent years. The amount of the use of robots, especially robotic arms, is increasing rapidly. After large-scale improvements, some companies have abandoned simple traditional robotic arms that have been eliminated from the industry and cannot meet the demands of the industry but install more high-tech elements on the robotic arm for use. In the upgrade of the robot arm, whether it is for the system or hardware, or software, there are some breakthrough improvements. Some companies use visual sensors in robotic arms to find and detect target objects and perform actions. Due to the gradual improvement of visual recognition technology, visual recognition technology has been widely used. Based on the understanding of the field of the visual recognition robot arm and consulting a lot of literature, this paper summarizes the current situation of the existing visual recognition robot arm and analyzes the principle and design of the visual recognition grasping robot arm. This paper focuses on analyzing how the existing visual recognition analysis works, how the robot arm recognizes the coordinates of the object and analyzes the object, and then grabs the object and puts it into the corresponding position, to achieve flexible and smooth use, then put it into the industry. After understanding the current situation, this paper will discuss and analyze the existing CNN model and transformer model for visual recognition applications, analyze and explain the principles and characteristic analysis methods of these two models, while comparing the two models, analyze the advantages and disadvantages, and propose areas that can be optimized.