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Twisted Robots Navigate Complex Mazes Without Humans or Software



Image: NC State University

A team of researchers at North Carolina State University and the University of Pennsylvania have developed soft robots that can navigate complex environments without human intervention or computer software. 

The paper titled “Twisting for Soft Intelligent Autonomous Robot in Unstructured Environments,” was published in the Proceedings of the National Academy of Sciences

Demonstrating Physical Intelligence

Jie Yin is corresponding author of the research paper and an associate professor of mechanical and aerospace engineering at NC State.

“These soft robots demonstrate a concept called ‘physical intelligence,' meaning that structural design and smart materials are what allow the soft robot to navigate various situations, as opposed to computational intelligence,” says Yin.

The soft robots were constructed with liquid crystal elastomers in the shape of a twisted ribbon. When the ribbon is placed on a surface that is at least 55 degrees Celsius, the part of the ribbon touching the surface contracts. At the same time, the part of the ribbon that is exposed to air stays the same. This results in a rolling motion, and as the surface gets warmer, the faster the robot rolls. 

“This has been done before with smooth-sided rods, but that shape has a drawback — when it encounters an object, it simply spins in place,” says Yin. “The soft robot we've made in a twisted ribbon shape is capable of negotiating these obstacles with no human or computer intervention whatsoever.”

Rotini-like twisted soft robots self-navigate mazes

Achieving Navigation Without Software

The robot is able to carry this out in two ways. When one end of the ribbon encounters an object, it rotates slightly to maneuver around the obstacle. When the central part of the robot encounters an object, it “snaps,” which is a rapid release of stored deformation energy that causes the ribbon to jump slightly and reorient itself before landing. The ribbon is able to snap more than one time before finding the right orientation that enables it to negotiate the obstacle. 

“In this sense, it's much like the robotic vacuums that many people use in their homes,” Yin says. “Except the soft robot we've created draws energy from its environment and operates without any computer programming.”

Yao Zhao is first author of the paper and a postdoctoral researcher at NC State. 

“The two actions, rotating and snapping, that allow the robot to negotiate obstacles operate on a gradient,” says Zhao. “The most powerful snap occurs if an object touches the center of the ribbon. But the ribbon will still snap if an object touches the ribbon away from the center, it's just less powerful. And the further you are from the center, the less pronounced the snap, until you reach the last fifth of the ribbon's length, which does not produce a snap at all.”

The team of researchers carried out multiple experiments showing how the ribbon-like robot can navigate a variety of maze-like environments. They also demonstrated that the soft robots would work effectively in desert environments since they can climb and descend slopes of loose sand. 

“This is interesting, and fun to look at, but more importantly it provides new insights into how we can design soft robots that are capable of harvesting heat energy from natural environments and autonomously negotiating complex, unstructured settings such as roads and harsh deserts.” Yin says.

The paper was also co-authored by NC State Ph.D. students Yinding Chi, Yaoye Hong and Yanbin Li; as well as Shu Yang, who is the Joseph Bordogna Professor of Materials Science and Engineering at the University of Pennsylvania. 


Alex McFarland is a Brazil-based writer who covers the latest developments in artificial intelligence. He has worked with top AI companies and publications across the globe.