Machine Vision: Not Just for Metrology Anymore

Control Engineering -- September 28, 2006 -- A couple of demonstrations that GE Fanuc set up in its booth at the International Manufacturing Technology Show in Chicago really showed how useful machine vision (MV) can be in machine-tool applications. In one demonstration, MV-guided robots moved parts along a simulated processing line that included milling and deburring stations. The second used two synchronized robots to assemble a gear box.

Watching robots on the simulated assembly line held all the fascination of peeking through a knothole to watch activity at an urban construction site. Being an old vision maven, the part I found most fascinating was watching two uncoordinated robots load parts onto a CNC miller.

By ‘‘uncoordinated’‘ I don't mean to imply ‘‘clumsy’‘ in any way. Uncoordinated robots work entirely independently driven by two separate controllers. They pass a minimum of signals between them, just as two humans in a bucket brigade need to pass only two bits between them: the downstream human only signals ‘‘I'm ready for another bucket,’‘ and the upstream human simply indicates ‘‘Here's another bucket.’‘

In the demonstration, the first robot picked randomly oriented parts from a large tote bin. Before gripping the next part to pick it up, the robot had to:

1. Locate all the available parts in the bin;
2. Choose the next part to pick up;
3. Figure out the best gripper orientation for picking the part up and moving it to a stand.

Task 1 is a lot more complex than one might think. The vision system has to recognize a lot of lumps in the bin, then match them with what the correct part would look like in various orientations—including upside down and laying on its side.

Once the MV system identified all the (correct) parts and their orientations in the bin, task 2 is not too bad. Simply choose the part that has the least number of interferences, or, if they're all equally easy to reach, the one that takes the least motion.

To accomplish task 3, just map out a series of joint movements that get the gripper onto the part, then place it right side up on the stand. Just write a few five-axis CNC programs and pick the one that accomplishes the task with the fewest number of motions. It's a piece of cake, right?

That's the first robot's job. The second robot knows the part is now on the stand right side up. It does not, however, know its orientation around the vertical axis or its exact position in the horizontal plane. It uses MV to get that information, then moves its gripper in for the exact grip it needs to put the part in the exact spot in x, y, z, ?, f space for the miller.

Adding the MV component to these robots makes them seem to come alive. It's like watching a cat track a ball of string while planning its pounce.

Even more impressive is the coordinated action of two robots assembling a gearcase. Coordinated robots work together very closely. It's like the coordination between a human's two hands, rather than a hand off between two humans.

In the demonstration, one robot picks up one half of the gearcase housing and presents it to the other robot. The second carefully inserts one gear and then the second on bearing shafts already in the housing. The robot has to rotate the second gear during the assembly to get the gear teeth to mesh properly.

The second robot then picks up the housing's second half and locks it in place over the assembly. The two robots then—together—reorient the housing to present it to a third robot, which inserts and drives the screws that ultimately hold the gearcase together. This third robot, of course, has to be run by the same controller as the first two in order to gain the precision needed.

These demonstrations show how versatile robotic equipment can be when guided by machine vision. Instead of dumb brutes fumbling around in the dark, MV-equipped robots can complete sophisticated tasks in the face of a large amount of environmental uncertainty. Their flexibility can rival that of human assemblers, and they don't leave forgotten lunch pails in parts bins.

—C.G. Masi, Control Engineering senior editor

© 2006, Reed Business Information, a division of Reed Elsevier Inc. All Rights Reserved.