When discussing the increasingly popular topics of Industry 4.0 and smart manufacturing, robots are an unavoidable issue. The level of intelligence in robots influences the entire process of industrial evolution. Traditional robots can only execute predetermined commands in strictly defined structured environments, lacking the ability to perceive and adapt to their environment, which greatly limits their application. Utilizing robot vision control eliminates the need for pre-teaching or offline programming of industrial robot trajectories, saving significant programming time and improving production efficiency and processing quality. This is what we refer to in the title: machine vision-based industrial robot positioning technology. This technology was first applied in China to tracking weld seams in welding robots, and Vision Image's visual acquisition equipment and image processing software have become pioneers and the preferred choice for vision guidance in the industry.
A typical robot vision positioning system is shown in Figure 1. A single camera is mounted at the end effector of an articulated robot, ensuring the workpiece appears completely within the camera's image. The system includes a camera system and a control system.
(1) Camera System: Consists of a single camera and a computer (including an image acquisition card), responsible for acquiring visual images and implementing machine vision algorithms. Given the current level of technological development in the industry, digital cameras are a more ideal choice. Among them, Vision Image's MV-EM/E series industrial cameras offer a rich set of development package functions, covering a wide range of resolutions and frame rates, and boasting good versatility and stability. Therefore, they are our top recommended choice.
(2) Control system: Composed of a computer and a control box, it is used to control the actual position of the robot's end effector. The working area is photographed by a CCD camera, and the computer extracts tracking features through image recognition methods, performs data recognition and calculation, obtains the position error values of each joint of the robot through inverse kinematics, and finally controls the high-precision end effector to adjust the robot's pose.
Figure 1. Composition of the robot vision positioning system
Let's analyze the working principle of a vision-guided robot in detail. First, a CCD camera (including image acquisition devices such as lenses) inputs video signals into a computer, which then processes them rapidly using software. The processing is as follows: a local image of the object being tracked is selected; this step is equivalent to offline learning, establishing a coordinate system in the image and training the system to find the object to track. After learning, the camera continuously acquires images, extracts tracking features, performs data recognition and calculation, and obtains the given values for the robot's joint positions through inverse kinematics. Finally, a high-precision end effector is controlled to adjust the robot's pose. The workflow is shown in the figure below.
Visual positioning system software flowchart
In this way, the visual positioning system combines region-based matching and shape feature recognition for data identification and calculation. This allows for the rapid and accurate identification of object boundaries and centers. The robot control system then uses inverse kinematics to solve for the angular errors of each joint, finally controlling a high-precision end effector to adjust the robot's pose and eliminate these errors. This solves the problem of a large discrepancy between the actual and desired positions of the robot's end effector, improving the positioning accuracy of traditional robots.
