Scientists have created a design and fabrication tool for soft pneumatic actuators for integrated
sensing, which can power personalized health care, smart homes, and gaming.
Soft, pneumatic actuators might not be a phrase that comes up in daily conversations, but
more likely than not you might have benefited from their utility.
The devices use compressed air to power motion, and with sensing capabilities, they've proven
to be a critical backbone in a variety of applications such as assistive wearables,
robotics, and rehabilitative technologies.
But there’s a bit of a bottleneck in creating the little dynamic devices that have advantages
like high response rates and power to input ratios.
They require a manual design and fabrication pipeline, which translates to a lot of trial
and error cycles to actually test and see whether the designs will work.
Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL)
devised a scalable pipeline to computationally design and digitally fabricate soft pneumatic
actuators, called “PneuAct.”
PneuAct uses a machine knitting process — not dissimilar to your grandma's plastic needle
knitting — but this machine operates autonomously.
A human designer simply specifies the stitch and sensor design patterns in software to
program how the actuator will move, and it can then be simulated before printing.
The textile piece is fabricated by the knitting machine, which can be fixed to an inexpensive,
off-the-shelf rubber silicone tube to complete the actuator.
The knitted actuator integrates conductive yarn for sensing, allowing the actuators to
"feel" what they touch.
The team cooked up several prototypes spanning an assistive glove, a soft hand, an interactive
robot, and a pneumatic walking quadruped.
Their devices were wrapped in a soft, yellow fabric that made them look a little bit like
banana fingers.
While there’s been plenty of movement in the hardware development of soft pneumatic
actuators over the years — a 2019 prototype of a collaborative robot used such actuators
to reproduce human-like gripping in its hands — the design tools haven’t improved with
quite as much speed.
Old processes have typically used polymers and molding, but the scientists used a combination
of elastic and sensing stitches (with conductive yarn) that allows for programming bending
of the devices when they’re inflated, and the ability to incorporate real world feedback.
For example, the team used the actuators to build a robot that sensed when it was touched
specifically by human hands, and reacted to that touch.
The team’s glove can be worn by a human to supplement finger muscle movement, minimizing
the amount of muscle activity needed to complete tasks and motions.
This could hold a lot of potential for those with injury, limited mobility, or other trauma
to the fingers.
The method can also be used to make an exoskeleton (wearable robotic units controlled by a computer
that supplement human motion and restore locomotion and movement); to that end the researchers
made a sleeve that can help wearers bend their elbow, knee, or other body parts.
One type of sensing the team incorporated was called “resistive pressure sensing,”
where the actuator “sends” pressure.
When fabricating a robotic gripper, it would try to grab onto something, and the pressure
sensor would sense how much force was being applied to the object, and then it would try
to see whether the grasp was successful or not.
The other type is “capacitive sensing,” where the sensor discerns some information
on the materials that the actuator is getting in touch with.
The actuators are sturdy — no yarn was harmed in the process.
One limitation of the system is that they were limited to tube-shaped actuators, because
it’s very easy to buy them off the shelf.
A logical next step is exploring actuators of different shapes, to avoid being constrained
by that single structure.
Another extension the scientists will explore is extending the tool to incorporate a task-driven,
optimization-based design, where users can specify target poses and optimal stitch patterns
that can be automatically synthesized.