Now Lights Bulbs Can Be Screwed In For You By This Soft Robotic Gripper – ZMR Blog
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Now Lights Bulbs Can Be Screwed In For You By This Soft Robotic Gripper

Now Lights Bulbs Can Be Screwed In For You By This Soft Robotic Gripper

What number of robots is required to fix a light bulb? Yes, merely one, supposing you are mentioning about a new robotic gripper designed by the engineers’ team at the University of California San Diego. A gripper is developed by the team that can manipulate and pick up objects without requiring to look at them as well as requiring to be coached.

The gripper is distinctive as it brings together 3 diverse capabilities. It can sense objects; it can build prototypes of the objects it is manipulating, and it can twist objects. This enables the gripper to function in low visibility and low light settings, for instance. The gripper was validated by the research team on an industrial Fetch Robotics robot and showed that it can manipulate, pick up, and model a huge variety of entities, from screwdrivers to light bulbs.

Michael T. Tolley, study lead, said, “We developed the device to imitate what happens when you reach into your pocket and feel for your keys.” There are 3 fingers equipped to the gripper. Each finger is composed of 3 soft malleable pneumatic chambers that on applying air pressure moves. This offers the above one degree of freedom to the gripper, so it can in real manipulate the entity it is holding. For instance, the gripper can screw in light bulbs, even hold paper pieces, and turn screwdrivers, thanks to this advance.

Soft Robotic Gripper

Additionally, every finger is enclosed with a smart, sensing skin—composed of silicone rubber, wherein sensors fabricated from conducting carbon nanotubes are entrenched. The rubber sheets are then rolled up, wrapped, and adorned into the flexible fingers to conceal them like skin. As the fingers move, the nanotube’s conductivity alters, which allows the sensing to detect and record when the fingers are flexing and coming in contact with an entity.

The data produced by the sensors is transferred to a control board that places the data together to generate the model’s 3D the gripper is manipulating. It is a technique identical to a CT scan, wherein 2D picture slices combine to a 3D image. The innovations were possible owing to the diverse know-how and experience of the team in the manufacturing and soft robotics fields, Tolley said.

Next steps consist adding artificial intelligence and machine learning to data processing in order to make the gripper capable of actually identifying the objects it is manipulating, instead of just modeling them. The team is also scrutinizing the use of 3D printing to make the fingers of the gripper more durable.


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