To test the algorithm, the researchers fitted the jamming gripper and a Microsoft Kinect 3D camera onto an industrial robot arm. The robot was tested in attempts to pick up 23 different objects, including toys, tools, and dishes. In these tests, the robot's success rate averaged 90 percent to 100 percent.
In most cases, the robot arm could successfully grasp new objects that it had not reviewed during its training. When the team ran the same tests with a simple directive to pick up an object at its center, the robot arm scored only had a 30 percent to 50 percent success rate. The exception was in picking up flat objects. With these, both the learned grasps and simple center grasps tied at an 89 percent success rate. The algorithm was also tested with the standard parallel jaws that most modern robots use, which produced similar results.
The team, which includes graduate students Yun Jiang and John Amend, presented their results May 16 in a paper at the International Conference on Robotics and Automation in St. Paul, Minn.
Jack, that's a good point about the use case of slight changes in the expected location of the object to be picked up. The main advantage the researchers cited was in adapting to different shaped and oddly shaped objects and being able to pick them up without dropping them (or spilling water from them as shown in the photo).
Agree....Most of the comments are based on environments where uniform parts are pre-aligned. Many times that's fine, but what if electronic components, gears, etc. could be "loose" and gripped and oriented by more sophisticated robotics? It could result in net savings. Another application is when the component shapes or orientation are irregular and poorly defined- logs, chicken wings, gemstones, or debris on the seabed.
At the Design News webinar on June 27, learn all about aluminum extrusion: designing the right shape so it costs the least, is simplest to manufacture, and best fits the application's structural requirements.
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 radio show will show what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.