MIT CSAIL creates materials capable of detecting their movement

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MIT researchers have created a 3D-printed material with built-in sensors that can detect its movement. | Source: MIT/CSAIL

Researchers at MIT Computer science and artificial intelligence laboratory (CSAIL) have developed programmable materials capable of detecting their own movements. The team created lattice materials with networks of air-filled channels, which allows researchers to measure changes in air pressure in the channels when the material is moved or bent.

The lattice structure created by the team is a kind of architectural material, which means that when you change the geometry of the material’s characteristics, its mechanical properties, such as stiffness or toughness, are changed. For a truss, the denser the network of cells making up the structure, the stiffer it is.

It is difficult to integrate sensors into these materials because of the scattered and complex shapes that compose them. However, placing sensors outside the structure does not provide enough information to get a full picture of how the material is deforming or moving.

The CSAIL team used digital light processing 3D printing to incorporate the air-filled channels into the struts that form the lattice structure of the team material. The researchers extracted the structure from a bath of resin and hardened it into a precise shape using projected light. In this method, an image is projected onto the wet resin and the light-structured areas are hardened. Researchers used pressurized air, vacuuming, and complex cleaning to remove any excess resin before it hardened.

When the resulting structure is moved or compressed, the channels formed by the 3D printing are deformed, which changes the volume of air inside. The team used a standard pressure sensor to measure these pressure changes and gain insight into how the material deforms.

The CSAIL team then built on their findings by integrating sensors into a class of materials developed for motorized soft robotics called manual shear auxetic (HSA). HSAs can be twisted or stretched, making them perfect for soft robotic actuators. Like architectural materials, HSAs are difficult to integrate into sensors due to their complex structure.

The team ran the sensorized HSA material through a series of movements for more than 18 hours and used the sensor data they collected to train a neural network to accurately predict the robot’s movement.

In the future, the team hopes their technology can be used to create soft and flexible robots with built-in sensors. These robots could understand their own posture and movements. The CSAIL team also sees the potential for using their technology to create wearable devices that provide information about how the user moves or interacts with their environment.

The team recently published the results of their study in Scientists progress. Daniela Rus, Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of CSAIL, was the lead author of the paper. Co-authors included MIT CSAIL graduate student Lillian Chin, former CSAIL postdoctoral fellow and now assistant professor at Northwestern University Ryan Truby, and CSAIL graduate student Annan Zhang.

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