Swarming microrobots self-organize into diverse patterns

Date:
June 6, 2023
Source:
Cornell University
Summary:
A research collaboration between Cornell and the Max Planck
Institute for Intelligent Systems has found an efficient way
to expand the collective behavior of swarming microrobots:
Mixing different sizes of the micron- scale 'bots enables them to
self-organize into diverse patterns that can be manipulated when a
magnetic field is applied. The technique even allows the swarm to
'cage' passive objects and then expel them.


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FULL STORY ==========================================================================
A research collaboration between Cornell and the Max Planck Institute for Intelligent Systems has found an efficient way to expand the collective behavior of swarming microrobots: Mixing different sizes of the
micron-scale 'bots enables them to self-organize into diverse patterns
that can be manipulated when a magnetic field is applied. The technique
even allows the swarm to "cage" passive objects and then expel them.

The approach may help inform how future microrobots could perform
targeted drug release in which batches of microrobots transport and
release a pharmaceutical product in the human body.

The team's paper, "Programmable Self-Organization of Heterogeneous
Microrobot Collectives," published June 5 in Proceedings of the National Academy of Sciences.

The lead author is Steven Ceron, Ph.D. '22, who worked in the lab of
the paper's co-senior author, Kirstin Petersen, assistant professor and
an Aref and Manon Lahham Faculty Fellow in the Department of Electrical
and Computer Engineering in Cornell Engineering.

Petersen's Collective Embodied Intelligence Lab has been studying
a range of methods -- from algorithms and classical control to
physical intelligence -- to coax large robot collectives into behaving intelligently, often by leveraging the robots' interactions with their environment and each other. However, this approach is exceedingly
difficult when applied to microscale technologies, which aren't big
enough to accommodate onboard computation.

To tackle this challenge, Ceron and Petersen teamed up with the paper's
co- authors, Gaurav Gardi and Metin Sitti, from the Max Planck Institute
for Intelligent Systems in Stuttgart, Germany. Gardi and Sitti specialize
in developing microscale systems that are driven by magnetic fields.

"The difficulty is how to enable useful behaviors in a swarm of robots
that have no means of computation, sensing or communication," Petersen
said. "In our last paper, we showed that by using a single global signal
we could actuate robots, in turn affecting their pairwise interactions to produce collective motion, contact- and non-contact-based manipulation
of objects. Now we have shown that we can expand that repertoire of
behaviors even further, simply by using different sizes of microrobots together, such that their pairwise interactions become asymmetric."
The microrobots in this case are 3D-printed polymer discs, each roughly
the width of a human hair, that have been sputter-coated with a thin
layer of a ferromagnetic material and set in a 1.5-centimeter-wide pool
of water.

The researchers applied two orthogonal external oscillating magnetic
fields and adjusted their amplitude and frequency, causing each microrobot
to spin on its center axis and generate its own flows. This movement in
turn produced a series of magnetic, hydrodynamic and capillary forces.

"By changing the global magnetic field, we can change the relative
magnitudes of those forces, " Petersen said. "And that changes the
overall behavior of the swarm." By using microrobots of varying size,
the researchers demonstrated they could control the swarm's level of self-organization and how the microrobots assembled, dispersed and
moved. The researchers were able to: change the overall shape of the
swarm from circular to elliptical; force similarly sized microrobots
to cluster together into subgroups; and adjust the spacing between
individual microrobots so that the swarm could collectively capture and
expel external objects.

"The reason why we're always excited when the systems are capable
of caging and expulsion is that you could, for example, drink a vial
with little microrobots that are completely inert to your human body,
have them cage and transport medicine, and then bring it to the right
point in your body and release it," Petersen said. "It's not perfect manipulation of objects, but in the behaviors of these microscale
systems we're starting to see a lot of parallels to more sophisticated
robots despite their lack of computation, which is pretty exciting."
Ceron and Petersen used a swarming oscillator model -- or swarmalator
-- to characterize precisely how the asymmetric interactions between different-sized disks enabled their self-organization.

Now that the team has shown that the swarmalator fits such a complex
system, they hope the model can also be used to predict new and previously unseen swarming behaviors.

"With the swarmalator model, we can abstract away the physical
interactions and summarize them as phase interactions between swarming oscillators, which means we can apply this model, or similar ones,
to characterize the behaviors in diverse microrobot swarms," said
Ceron, currently a postdoctoral fellow at Massachusetts Institute of Technology. "Now we can develop and study magnetic microrobot collective behaviors and possibly use the swarmalator model to predict behaviors
that will be possible through future designs of these microrobots."
"In the current study, we were programming differences between exerted
forces through the microrobots' size, but we still have a large parameter
space to explore," he said. "I'm hoping this represents the first in a
long line of studies in which we exploit heterogeneity in the microrobots' morphology to elicit more complex collective behaviors." The research
was supported by the Max Planck Society, the National Science Foundation,
the Fulbright Germany Scholarship and the Packard Foundation Fellowship
for Science and Engineering.

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========================================================================== Story Source: Materials provided by Cornell_University. Original written
by David Nutt, courtesy of the Cornell Chronicle. Note: Content may be
edited for style and length.


========================================================================== Journal Reference:
1. Steven Ceron, Gaurav Gardi, Kirstin Petersen, Metin
Sitti. Programmable
self-organization of heterogeneous microrobot
collectives. Proceedings of the National Academy of Sciences,
2023; 120 (24) DOI: 10.1073/ pnas.2221913120 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/06/230606111700.htm

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