Soft Actuators Go From Squishy to Stiff (and Back Again)

Jamming layers give a soft actuator adjustable stiffness

2 min read
Soft Actuators Go From Squishy to Stiff (and Back Again)
Photo: Evan Ackerman/IEEE Spectrum

Soft actuators are appealing for robotics because they’re cheap (made out of plastics or polymers and air), inherent compliant and relatively safe for humans to interact with, and able to adapt themselves to grip a wide range of objects. Being soft does tend to make them by definition bad at being hard, so for those times when you need an actuator with some stiffness, well, that’s just too bad.

Or is it?

Researchers at Technische Universitat Berlin led by Professor Oliver Brock have combined soft pneumatic actuators with a jamming system that results in a variable-stiffness actuator that’s soft when you want and hard when you want.

The researchers tested three different jamming systems, including the traditional coffee grounds, as well as two other designs based on scales and interleaved layers [see image below]. They eventually settled on the interleaved layers for the final design, because it requires far less pressure to jam, although it’s more complex to manufacture. In principle, it works in a similar manner: when the interleaved layers have air between them, they can slide against eachother, allowing the actuator to flex. When the air is pumped out, the layers compress against eachother, and the actuator is stiffened. You can do this by hand with a syringe, or using a pump or autonomous operation.

img The three jamming systems tested by the researchers: granular jamming; layer jamming with overlapping fish-scale-like layers; and layer jamming with stacks of interleaved layers. The jamming chambers are indicated by dashed lines, and the Pneuflex actuator is shown on the bottom. (a) Unjammed chambers result in flexible actuators. (b) Jammed chambers result in stiff actuators. Image: TU Berlin

Overall, the jammed actuator exhibited a stiffness increase of 8x, resulting in an application of force increase of 2.3x, which is pretty significant. Incidentally, you can make these actuators yourself for free: they’re called Pneuflex actuators, and instructions are available here.

“Selective Stiffening of Soft Actuators Based on Jamming,” by Vincent Wall, Raphael Deimel, and Oliver Brock from the Robotics and Biology Laboratory at TU Berlin was presented at ICRA 2015 in Seattle, Wash.

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Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman

“I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

This article is part of our special report on AI, “The Great AI Reckoning.”

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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