Chemistry bends a robotic arm without electronics

Chemistry bends a robotic arm without electronics

Organisms, from bacteria to humans, are independent in large part due to movement – converting stored energy into slowly repeating electrical and mechanical signals that allow organisms to move.

But these types of relatively slow movements, such as the flapping of a fly’s wing or a person running, called low-frequency oscillations, are difficult to recreate in electronic devices. The researchers’ inability to do so created a major obstacle in developing fully autonomous microrobots.

Now, a team led by Northwestern University and the Massachusetts Institute of Technology (MIT) has discovered a way to create slow motions using chemistry alone and applied it to precision robotics. This discovery also comes with a new understanding of thermodynamics as asymmetry and heterogeneity benefit a system.

Thomas A. said: Berueta, co-first author and Ph.D.: “There are currently no methods for generating slow vibrations in microrobots.” Candidate at the Center for Robotics and Biosystems at Northwestern. “To do this with electronic devices would require much more computation than is possible at these metrics.”

The results will be published October 6 in the journal Nature Communications. Berruta’s co-first author, Jing Fan Yang, is a graduate student at the Massachusetts Institute of Technology.

To conduct the study, the engineers first created an oscillator consisting of the interactions of a pair of energetic microparticles that were settled on top of a drop of hydrogen peroxide. To the team’s surprise, the more particles were introduced, the more stable the motion – but only when the particles had very different levels of chemical reaction. To understand this “system caused by asymmetry,” the team developed a new thermodynamic theory that explains the appearance of such behaviors in their system and many others.

“Our idea arose from the idea that the design principles that allow us to make reliable machines at large scales may not actually apply to the scales we operate with,” Berrueta said. “Think of biology, where we know that the molecular machinery of cells is very powerful despite it being messy and imprecise.”

Precision engineering, a strategy that involves designing highly precise machines, is the dominant technology in many fields of engineering. But Berrueta, inspired by the research of Adilson Motter, a professor of physics and astronomy at Northwestern Weinberg College of Arts and Sciences who studies asymmetry in complex networks and systems, tried a different strategy.

After discovering that asymmetry can also be useful for creating regular, spontaneous movements, the team put their theory to the test using a small robotic arm. By introducing a more reactive particle (and thus breaking the symmetry) and adding more standard particles, the researchers found that they could create consistent, periodic motion in a microbottom arm.

Moving away from micro-engineering could benefit more projects and fields and drive future autonomy in small robots, said Todd de Murphy, professor of mechanical engineering at Northwestern University’s McCormick School of Engineering and senior author of the paper and a Berruta consultant.

“There is an assumption that similarity is better in geometry,” Murphy said. “But we need to make sure that precision-guided methods actually help the applications we care about. In some cases, irregular parts will be better — and it may be cheaper to make components that aren’t quite the same.”

The paper sparked new ideas across the collaboration, and Berrueta said he’s excited to see what other places the team’s ideas will apply.

“We will continue to cooperate,” Berueta said. “The idea of ​​imprecise design is as scalable as it gets. We expect it to be very fruitful in the areas of robotics and artificial intelligence, as well as micro and nano engineering.”

The paper, Emerging Microoscillators Through System Induced Asymmetry, received funding from a US Army Research Office Interdisciplinary University Research Initiative grant on Formal Foundations of Algorithmic Matter and Emerging Computation (grant W911NF-19-1-0233) and the US United Department of Energy, Office of Science, Basic Energy Sciences (grant DE-FG02-08ER46488).

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