Aug. 9, 2021 — Biohybrid robots – made by adding living tissues such as muscle cells to an artificial skeleton – have a lot of potential for doing complex jobs at tiny scales. But engineering these robots has been hit or miss, with researchers testing more or less arbitrary designs. A team from the University of Illinois Urbana-Champaign used simulations on the XSEDE-allocated Bridges platform at the Pittsburgh Supercomputing Center (PSC) to create a rational design approach that they and others can use to more quickly and efficiently develop effective biohybrid robots.
Why It’s Important
In the 1966 movie Fantastic Voyage, a team of scientists undergoes sci-fi shrinking so they can enter the brain of a stricken scientist and save his life. Shrinking people will likely remain a fantasy. But scientists are now seriously planning to create miniature robots that can do much the same thing. In particular, incorporating living tissues into robots – such as muscle cells that can move them or nerve cells that can direct them – has the potential of making them smaller and smarter than they could be using electronics and artificial motors.
Progress on such biohybrid robots has been slower than researchers would like. One limitation has been human experts’ ability to design the best physical layout for such a robot. We might imagine a tiny, human shape. But such a shape wouldn’t be the best for the mission – particularly at tiny scales. Jiaojiao Wang and Xiaotian Zhang, graduate students studying with advisors Rashid Bashir and Mattia Gazzola at the University of Illinois Urbana-Champaign, respectively, wondered whether they could improve the design of “walkers” – robots that walk to move themselves – by combining computer simulations with lab testing. Their simulations would test many different combinations of body architecture, muscle-fiber arrangements and ways to electrically trigger the muscle fibers to move, saving lab time and expense. But they would require massive computation. The XSEDE-allocated Bridges advanced research computing platform at PSC made this work possible.
The Illinois team based their designs on a “skeleton” formed from 3D-printed PEGDA hydrogel. This flexible polymer structure would provide the body and legs of tiny walkers only 6 millimeters (about a fifth of an inch) in size. They also molded muscle cells in a ring shape that would wrap around each leg in the walker like a rubber band, contracting to make the legs move. They signaled the robots to move through a water solution by putting electrodes in the water, changing the electric field to make the muscle rings respond to the field. The skeleton’s legs were asymmetrical, shorter on one side so that the muscle ring contractions would make them move more in one direction than the other.
The team started with three basic designs. In the three-legged “three-ring” design, each leg connected with a central attachment point via a muscle ring. In a four-legged “four-ring” design shaped like a cross, each leg also connects to a central attachment via a muscle ring. Finally, in a four-legged “two-ring” walker, legs paired like in a tiny horse, a muscle ring connected each pair of opposing legs.
How XSEDE Helped
Using Bridges’ computing power, Zhang tested the three designs against each other as well as different arrangements of the muscle bands, and different patterns and strengths of electrical fields for their ability to move the robots. By conducting thousands of these simulations, he narrowed the choices to the most effective design, which Wang then synthesized and tested in the real world.
In the simulations, the three-ring version proved to be the fastest mover, but it had more trouble walking in a straight line than the others. The two-ring model was nearly as fast. It was also the best at walking a straight line, displaying the greatest reliability and the sharpest intentional turns. By fabricating the two-ring model, Wang was able to test various electrical signals to optimize control over the little robots and create more effective movement. More importantly, they had established a design approach that they and other researchers could use to efficiently engineer more complex biological machines with better controllability and adaptivity. The team would like to refine their designs further, possibly adding neural tissue to give the robots the ability to change behavior and react to their environment.
This study was jointly funded by NSF EFRI C3 SoRo No. 1830881, NSF CAREER No. 1846752 (M.G.), and Strategic Research Initiatives program of the University of Illinois at Urbana-Champaign. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges system, which was supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center (PSC).
Click here to learn more.
Source: Ken Chiacchia, Pittsburgh Supercomputing Center