MIT’s ‘programmable materials’ can sense their own movements

New ‘programmable materials’ developed at the Massachusetts Institute of Technology (MIT) can sense how they are moving and interacting with the environment, according to their creators.

With applications in soft robotics or wearable smart devices, the ‘sensing structures’ were 3D-printed by researchers using just one material.

Incorporated networks of air-filled channels were built into the lattice materials 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. 

Composed of single cells in a repeating pattern, the material’s mechanical properties – such as stiffness or hardness – can be altered by changing the size or shape of the cells. A denser network of cells makes a stiffer structure, for example.

This technique could create flexible soft robots with embedded sensors that understand their own posture and movements, the researchers said. It might also be used to produce wearable smart devices, such as customised running shoes that provide feedback on how an athlete’s foot is impacting the ground.

“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 sensorisation with structure. And if you use really complex materials, then you can have motion, perception, and structure all in one,” said Lillian Chin, co-lead author of a paper on the work.

The researchers measure the pressure changes with an off-the-shelf pressure sensor, which gives feedback on how the material is deforming. Because they are incorporated into the material, the ‘fluidic sensors’ are more accurate than sensors placed on the outside of a structure, the team said.

The team also incorporated sensors into a new class of materials developed for motorised soft robots, known as handed shearing auxetics (HSAs). The materials can be twisted and stretched simultaneously, meaning they can be used as effective soft robotic actuators, but they are difficult to sensorise because of their complex forms.  

Using the researchers’ fluidic sensors and the assistance of a neural network, they could accurately measure the movement of a 3D-printed HSA robot as it went through a series of movements for more than 18 hours.

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

Applications for this approach could include tailored American football helmets with internal sensing, increasing the accuracy of feedback from on-field collisions and improving player safety.

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

The paper was published in Science Advances.