K-Decision Tree Control Method of Welding Robots based on Machine Vision

Welding robots (WR) have been used in many industries due to the problems of low efficiency of manual welding, long work, slow speed, and inability to determine the type of welding. However, WR often work through training and replication, and new skills are required to enable these modes of operation to be implemented in specific welding environments. The purpose of this paper is to study the k-decision tree control method of WR based on machine vision. This paper analyzes what is machine vision and the characteristics of machine vision, and also analyzes the geometric transformation and camera selection in machine vision. Finally, the experimental conclusion is drawn. The empirical analysis results show that the traditional WR k decision tree control system becomes more concise and convenient after using machine vision technology. The WR k decision tree control system is applied to machine vision technology, and it is found that the design efficiency has become higher.