Machine vision has a wide range of applications in industrial automation: 2D Robot Vision. 2D vision systems use line-scan or area-scan cameras to capture photographic images that contain width and length, but no depth. By processing these images, they measure the visible characteristics of an object, and feed robotic handling systems data on its position, rotational orientation, and type.
Manufacturing industries have been making use of robots and automation on a very large scale. Robots have been successful in meeting the requirements of precision, endurance, speed, and reliability. Robots perform all kinds of dangerous and dirty jobs. Robots also handle the manufacturing work which includes material handling (pick and place), welding, packaging, assembling, painting, palletizing, product testing and inspection.
Pharmaceutical companies use machine vision systems in automated production lines to inspect injection needles, which are unusable if blunt or bent. Multiple cameras photograph needles as they flow through the system on powered conveyors. Sophisticated computer software analyses the captured images to determine needle sharpness and check the contour of the tube. Industrial robots use this information to separate and discard defect needles.
There are many works that robot can carry out but some work only human beings can do. No robot can replace any human being but robots can perform few works more efficiently which are repetitive. All you need to do is program them in the way you want them to work. Many industries have been making use of these robots for many purposes like manufacturing, automotive, biotech, pharmaceutical and machinery.
Nearly every day a news story is published that proclaims the U.S. manufacturing industry is dead and little if any manufacturing hiring is occurring. Take these claims with a grain of salt. U.S. manufacturers are thriving. But they are not the low tech, unskilled-labor businesses of twenty or thirty years ago. They have integrated so much technology into the manufacturing process that they require highly trained employees.