Abstract
This article presents a study of algorithms using machine vision to control a robot manipulator. With the increasing number of robots used in industry and other industries, the need for reliable and accurate control algorithms has increased. Thus, the relevance of the topic increases, and research in this area can significantly improve the efficiency and safety of robotic systems. The purpose of this article is a comprehensive study of various control algorithms, as well as the integration of machine vision into control systems. Robot manipulator control algorithms are a set of mathematical procedures and methods that allow robots to perform certain movements and tasks with the necessary efficiency and accuracy. To do this, the robot receives important data about the world around it using machine vision. The article considers three main types of algorithms: inverse kinematics, PID controllers and machine learning algorithms. Reverse kinematics determines the angles of rotation of the robot joints, which are necessary to achieve a given position and orientation of the working tool. The PID controller controls the movements of the robot's joints. By controlling the speed and force, it corrects errors between the actual and the set position. Using machine learning methods allows you to learn new tasks and adapt your behavior to changing conditions. Within the framework of this study, the theoretical aspects of algorithms and machine vision will be considered. The research was carried out on the Optima 2 manipulator manufactured by ZARNITZA.