Cornell University researchers have developed a synthetic, stretchable optical lace that could enable robots to better sense and interact with their environments.
The synthetic material, developed by Ph.D. student Patricia Xu through Cornell’s Organic Robotics Lab, creates a linked sensory network similar to a biological nervous system. The research was published in a paper, “Optical Lace for Synthetic Afferent Neural Networks,” that appeared recently in Science Robotics.
“We want to have a way to measure stresses and strains for highly deformable objects, and we want to do it using the hardware itself, not vision,” said lab director Rob Shepherd, associate professor of mechanical and aerospace engineering, the paper’s senior author, in an article appearing on Cornell University’s website.
RELATED: Skin sensors transforming healthcare
For the optical lace project, Xu used a flexible, porous lattice structure manufactured from 3D-printed polyurethane. The core structure was threaded with stretchable optical fibers containing more than a dozen mechanosensors. An LED light was attached to illuminate the fiber. Pressing the lattice structure created deformations that enabled the sensors to trace changes in the photon flow.
Shepard envisions the optical lace providing a flesh-like material that robots could be fitted with, for applications such as medical robots to assist the elderly in performing tasks, as well as for industrial applications.
“The idea that robots could help take care of the elderly is very real,” Shepherd was quoted as saying. “The robot would need to know its own shape in order to touch and hold and assist elderly people without damaging them. The same is true if you’re using a robot to assist in manufacturing. If they can feel what they’re touching, then that will improve their accuracy.”
According to the researchers, the optical lace is less sensitive than human fingers but more sensitive than the human back. Because the material is washable, Shepherd’s lab has launched a startup company to commercialize Xu’s sensors to make garments that can measure a person’s shape and movements for augmented reality training.