Liwaiwai Liwaiwai



Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • About
  • Artificial Intelligence
  • Robotics
  • Technology

New Programmable Materials Can Sense Their Own Movements

  • August 12, 2022
  • relay
MIT researchers have developed a method for 3D printing materials with tunable mechanical properties, that sense how they are moving and interacting with the environment. The researchers create these sensing structures using just one material and a single run on a 3D printer.

To accomplish this, the researchers began with 3D-printed lattice materials and incorporated networks of air-filled channels into the structure during the printing process. By measuring how the pressure changes within these channels when the structure is squeezed, bent, or stretched, engineers can receive feedback on how the material is moving.

The method opens opportunities for embedding sensors within architected materials, a class of materials whose mechanical properties are programmed through form and composition. Controlling the geometry of features in architected materials alters their mechanical properties, such as stiffness or toughness. For instance, in cellular structures like the lattices the researchers print, a denser network of cells makes a stiffer structure.

This technique could someday be used to create flexible soft robots with embedded sensors that enable the robots to understand their posture and movements. It might also be used to produce wearable smart devices that provide feedback on how a person is moving or interacting with their environment.

“The idea with this work is that we can take any material that can be 3D-printed and have a simple way to route channels throughout it so we can get sensorization with structure. And if you use really complex materials, then you can have motion, perception, and structure all in one,” says co-lead author Lillian Chin, a graduate student in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

Joining Chin on the paper are co-lead author Ryan Truby, a former CSAIL postdoc who is now as assistant professor at Northwestern University; Annan Zhang, a CSAIL graduate student; and senior author Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of CSAIL. The paper is published today in Science Advances.

 

Architected materials

The researchers focused their efforts on lattices, a type of “architected material,” which exhibits customizable mechanical properties based solely on its geometry. For instance, changing the size or shape of cells in the lattice makes the material more or less flexible.

While architected materials can exhibit unique properties, integrating sensors within them is challenging given the materials’ often sparse, complex shapes. Placing sensors on the outside of the material is typically a simpler strategy than embedding sensors within the material. However, when sensors are placed on the outside, the feedback they provide may not provide a complete description of how the material is deforming or moving.

Instead, the researchers used 3D printing to incorporate air-filled channels directly into the struts that form the lattice. When the structure is moved or squeezed, those channels deform and the volume of air inside changes. The researchers can measure the corresponding change in pressure with an off-the-shelf pressure sensor, which gives feedback on how the material is deforming.

Because they are incorporated into the material, these “fluidic sensors” offer advantages over conventional sensor materials.

 

soft robotic finger
This image shows a soft robotic finger made from two cylinders comprised of a new class of materials known as handed shearing auxetics (HSAs), which bend and rotate. Air-filled channels embedded within the HSA structure connect to pressure sensors (pile of chips in the foreground), which actively measure the pressure change of these “fluidic sensors.” Image courtesy of the researchers

“Sensorizing” structures

The researchers incorporate channels into the structure using digital light processing 3D printing. In this method, the structure is drawn out of a pool of resin and hardened into a precise shape using projected light. An image is projected onto the wet resin and areas struck by the light are cured.

But as the process continues, the resin remains stuck inside the sensor channels. The researchers had to remove excess resin before it was cured, using a mix of pressurized air, vacuum, and intricate cleaning.

They used this process to create several lattice structures and demonstrated how the air-filled channels generated clear feedback when the structures were squeezed and bent.

“Importantly, we only use one material to 3D print our sensorized structures. We bypass the limitations of other multimaterial 3D printing and fabrication methods that are typically considered for patterning similar materials,” says Truby.

Building off these results, they also incorporated sensors into a new class of materials developed for motorized soft robots known as handed shearing auxetics, or HSAs. HSAs can be twisted and stretched simultaneously, which enables them to be used as effective soft robotic actuators. But they are difficult to “sensorize” because of their complex forms.

They 3D printed an HSA soft robot capable of several movements, including bending, twisting, and elongating. They ran the robot through a series of movements for more than 18 hours and used the sensor data to train a neural network that could accurately predict the robot’s motion.

Chin was impressed by the results — the fluidic sensors were so accurate she had difficulty distinguishing between the signals the researchers sent to the motors and the data that came back from the sensors.

“Materials scientists have been working hard to optimize architected materials for functionality. This seems like a simple, yet really powerful idea to connect what those researchers have been doing with this realm of perception. As soon as we add sensing, then roboticists like me can come in and use this as an active material, not just a passive one,” she says.

“Sensorizing soft robots with continuous skin-like sensors has been an open challenge in the field. This new method provides accurate proprioceptive capabilities for soft robots and opens the door for exploring the world through touch,” says Rus.

In the future, the researchers look forward to finding new applications for this technique, such as creating novel human-machine interfaces or soft devices that have sensing capabilities within the internal structure. Chin is also interested in utilizing machine learning to push the boundaries of tactile sensing for robotics.

“The use of additive manufacturing for directly building robots is attractive. It allows for the complexity I believe is required for generally adaptive systems,” says Robert Shepherd, associate professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University, who was not involved with this work. “By using the same 3D printing process to build the form, mechanism, and sensing arrays, their process will significantly contribute to researcher’s aiming to build complex robots simply.”

This research was supported, in part, by the National Science Foundation, the Schmidt Science Fellows Program in partnership with the Rhodes Trust, an NSF Graduate Fellowship, and the Fannie and John Hertz Foundation.

 

By Adam Zewe | MIT News Office
Source MIT

relay

Related Topics
  • 3D Printing
  • MIT
  • Sensors
  • Wearables
You May Also Like
View Post
  • Artificial Intelligence

Microsoft‘s Big AI Ambitions Go Beyond Just OpenAI And ChatGPT

  • February 3, 2023
View Post
  • Artificial Intelligence
  • Technology

Deepfakes: Faces Created By AI Now Look More Real Than Genuine photos

  • February 3, 2023
View Post
  • Artificial Intelligence

GPT-3 In Your Pocket? Why Not!

  • February 3, 2023
View Post
  • Artificial Intelligence
  • Design
  • Engineering

Can AI Replace Cloud Architects?

  • February 2, 2023
View Post
  • Artificial Intelligence

Meet Aiko And Aiden: The World’s First AI Interns

  • February 2, 2023
View Post
  • Artificial Intelligence
  • Technology

Google Scrambles To Catch Up In The Wake Of OpenAI’s ChatGPT

  • January 31, 2023
View Post
  • Artificial Intelligence
  • Technology

9 Ways We Use AI In Our Products

  • January 31, 2023
View Post
  • Technology
  • Tools

Google Cloud Unveils New AI Tools for Retailers

  • January 31, 2023
Stay Connected!
LATEST
  • 1
    Microsoft‘s Big AI Ambitions Go Beyond Just OpenAI And ChatGPT
    • February 3, 2023
  • 2
    Deepfakes: Faces Created By AI Now Look More Real Than Genuine photos
    • February 3, 2023
  • 3
    GPT-3 In Your Pocket? Why Not!
    • February 3, 2023
  • 4
    Can AI Replace Cloud Architects?
    • February 2, 2023
  • 5
    Meet Aiko And Aiden: The World’s First AI Interns
    • February 2, 2023
  • 6
    Google Scrambles To Catch Up In The Wake Of OpenAI’s ChatGPT
    • January 31, 2023
  • 7
    9 Ways We Use AI In Our Products
    • January 31, 2023
  • 8
    Google Cloud Unveils New AI Tools for Retailers
    • January 31, 2023
  • 9
    7 Ways Google Is Using AI To Help Solve Society’s Challenges
    • January 30, 2023
  • 10
    The Ethics Of Machine Learning: Understanding The Role Of Developers And Designers
    • January 30, 2023

about
About
Hello World!

We are liwaiwai.com. Created by programmers for programmers.

Our site aims to provide materials, guides, programming how-tos, and resources relating to artificial intelligence, machine learning and the likes.

We would like to hear from you.

If you have any questions, enquiries or would like to sponsor content, kindly reach out to us at:

[email protected]

Live long & prosper!
Most Popular
  • 1
    8 Best Human Behaviour Datasets For Machine Learning
    • January 30, 2023
  • 2
    Built With BigQuery: How To Accelerate Data-Centric AI Development With Google Cloud And Snorkel AI
    • January 29, 2023
  • 3
    What Kind Of Future Will AI Bring Enterprise IT?
    • January 29, 2023
  • 4
    Prompt Engineering For ChatGPT And Generative AI
    • January 29, 2023
  • 5
    AI Might Be Seemingly Everywhere, But There Are Still Plenty Of Things It Can’t Do—for now
    • January 27, 2023
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
  • About

Input your search keywords and press Enter.