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In recent years, the rapid advancement of robotics—especially industrial robots—has significantly boosted the demand for machine vision systems. At the World Robotics Congress in May 2016, Mao Weiming, Deputy Minister of the Ministry of Industry and Information Technology, emphasized that intelligent manufacturing would be a top priority for the department. He also outlined the "13th Five-Year Plan" for China's industrial robot industry, aiming to promote policies that support the development, production, and widespread use of key robotic components. As China advances its "Made in China 2025" strategy, the industrial intelligent robot market has experienced explosive growth. Machine vision, acting as the "eyes" of these robots, plays a crucial role in enhancing their flexibility and precision.
As an essential component of modern robots, particularly intelligent visual robots, machine vision is critical for improving a robot’s adaptability and operational efficiency. But how can one choose the right visual system based on specific needs?
First, cameras and optical components are fundamental. These include one or more cameras and lenses, which vary in type such as black and white, RGB color, progressive scan, or line scan, depending on the application. Lighting is equally important, as it ensures clear image capture. Common lighting options include LEDs, fluorescent lamps, and incandescent lights, each with different brightness levels and shapes.
Sensors, often in the form of gratings or proximity sensors, help trigger the vision system when an object is in position. Image acquisition cards, also known as video capture cards, connect the camera to a PC, converting signals into data the computer can process. They also allow control over camera settings like exposure and shutter speed.
The PC platform serves as the core of the system, typically requiring at least a Pentium processor for efficient image processing. Industrial-grade computers are preferred due to harsh factory environments. Detection software then processes the captured images, making decisions like PASS/FAIL, and comes in various forms such as C libraries, ActiveX controls, or graphical interfaces.
Digital I/O boards and network connections ensure communication between the vision system and external systems, allowing data transfer and integration with databases or PLCs. Proper configuration of a PC-based machine vision system requires careful planning, including defining objectives, determining required speed, selecting appropriate hardware, and choosing suitable software.
When selecting a camera, factors like color vs. black and white, movement of objects, and resolution must be considered. Poor optics or lighting can severely limit performance, no matter how advanced the system. The image acquisition card determines compatibility with the camera and affects data accuracy.
Machine vision systems lack the versatility of human perception, so understanding their limitations is key to avoiding errors. Software choice is vital, as it impacts both development time and system performance. Some systems offer graphical programming, while others require coding for complex tasks.
Finally, ensuring seamless communication with the production line and preparing for future upgrades are essential for long-term success. A well-designed machine vision system can greatly enhance quality control and operational efficiency, making it an indispensable part of modern manufacturing.