This technology seems to allow for a certain amount of forgiveness in the pick location for larger objects. It would be great to have a cell that can easily adjust to different parts as they come down the assembly line. This would allow for touchup free product changes. That would be great.
I agree I don''t know that I see an application for an adaptable gripper. But I do believe the ability of robots to pick up parts and hold them in place perhaps to be welded by another robot would be an application where this would be helpful. But on the typical assembly line the job is going to be repetitive and follow the same steps again and again without having to adapt.
I really liked the article. I don't know if I completely understand the inner workings of the pressure adaping inside of the big blue ball, but the statistics of success for picking up parts is pretty cool.
I have had a bit of experience with assembly lines. I can't think of any application for this gripper. Printed circuit board assembly needs very fast small part placement with vision compensation, or fast very fine placement of large parts with many leads, using vision compensation. I have only seen vacuum nozzles used. I can't see this gripper being used in a high-speed vision application. In automotive speed, accuracy, and payload are important. I don't think this gripper has any of these 3. Even where I have seen off-line programming using 3-D modeling, an actual human had to step through the program to touch-up positions and movements. Robots, aka Flexible Automation, vs. 'hard automation', was the answer to changing products. The gripper or 'end effector' is always customized to the application. The part must be both 'picked' and 'placed'.
To belabor the point: I don't think this gripper could pick up a 1mm x 2mm chip, take a vision shot, and then place it into a solder screened location, and do it again 1/10 second later. I also doubt that it could pick up a 50 lb bag of flour and place it to a pallet.
The gripper and the algorithm are interesting research, without a current practical application.
The point here is that, with a less expensive universal gripper, such as Cornell's, plus the algorithm the team invented, a robotic assembly line can quickly adapt to optimally picking up all kinds of new objects with different sizes and shapes that it's never encountered before. The alternative, which we've heard a lot about in DN articles and comments, is lengthy and expensive programming in 4D, presumably with highly specialized grippers. This would be a big benefit in assembly lines, especially those of EMS, which are continually changing products.
Right now this looks like a technology development seeking a solution. As the robots develop, solutions will appear. I've seen this notion of robots learning how to do things by trial and error. That's impressive.
Ann R Thryft; Yes, optimal vs. non-optimal is the clarification. For some applications the optimal gripper is vacuum cup(s). The human hand is a very versatile end effector. Duplicating it is not easy. There could be applications where this gripper would be optimal, but I don't think the water glass is one of them.
Glenn, thanks for that observation about the photo. I should have pointed out in the caption that this universal gripper, without the algorithm, can pick up objects but that this shows how it does so in a non-optimal manner, forming a "before" picture.
naperlou, not everyone thinks about how a robot would do things they themselves are doing. But that does sound like how engineers think. Thanks for the observation about the lack of sensors here--I think that's a good point, and it's interesting to know this isn't the only research team taking that approach.
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For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.