What if your objects are too big or awkward to teach to a robot vision system?
Is robot vision only suitable for small, regular items?
Why isn't it easy to teach every object every time?
Modern robot vision systems are extremely flexible. You can use them to detect a huge array of different object types, sizes, and shapes. Whether you are detecting circuit boards in a pick-and-place application, detecting parts for a machine tending application, or detecting boxes for a palletizing application, you can probably use robot vision.
Robot vision algorithms can be taught to recognize almost any object that shows up as a clear, distinct image in the camera view…
… and that's the problem.
Sometimes, you are working with objects that are too big, too awkward, and too strangely shaped to be easily detected by robot vision. You know that the robot has the capacity to manipulate the objects but the vision system just doesn't want to play ball.
Is it possible for robot vision systems to detect such objects?
Or are you restricted to only using small objects that fit easily with the camera view and have straight, regular outlines?
In the latest blog post from Robotiq, they outline why using CAD model teaching is the best way to teach these types of objects. Check it out.
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