LOOK: A Robot Hand With a Human Touch
The term "robotic" is often used to describe mechanical, rigid, or abrupt movements. Researchers from Cornell University have found a way to redefine this description by developing a soft robot that could feel its surroundings internally in a very similar way to humans.
Robert Shepherd, an assistant professor of mechanical and aerospace engineering and principal investigator of Organic Robotics Lab, along with the rest of the research group has released a study that features stretchable optical waveguides that could act as curvature, elongation and force sensors in a soft robotic hand.
The lead author of the study, Huichan Zhao, is a doctoral student whose work, "Optoelectronically Innervated Soft Prosthetic Hand via Stretchable Optical Waveguides," was included in the first edition of Science Robotics.
"Most robots today have sensors on the outside of the body that detect things from the surface," Zhao explained. "Our sensors are integrated within the body, so they can actually detect forces being transmitted through the thickness of the robot, a lot like we and all organisms do when we feel pain, for example."
Though optical waveguides had been in use since the early 1970s for numerous sensing functions, it was only with the introduction of soft lithography and 3-D printing that the process of fabrication became a less complex process. The availability of elastomeric sensors can now readily produce needed parts that are incorporated into a soft robotic application.
A four-step soft lithography process was used to produce the light-propagating core and cladding, or the outer surface of the waveguide, housed the LED and the photodiode. As the prosthetic hand deforms, more light is lost through the core. That variable loss of light, as detected by the photodiode, is what allows the prosthesis to "sense" its surroundings.
"If no light was lost when we bend the prosthesis, we wouldn't get any information about the state of the sensor," Shepherd shared. "The amount of loss is dependent on how it's bent."
Using optoelectronic prosthesis to perform a variety of tasks, the hand was able to scan three tomatoes and identify, by softness, which was the ripest by grasping and probing for both shape and texture.