Could a Swarm of Nanorobots Show Collective Intelligence?
From a distance they looked like clouds of dust. Yet the swarm of microrobots in author Michael Crichton’s bestseller “Prey” were self-organized. It acted with rudimentary intelligence, learning, evolving and communicating with itself to become more powerful.
A new model by a team of researchers led by Penn State and inspired by Crichton’s novel describes how biological or technical systems form complex structures equipped with signal processing capabilities that enable the systems to respond to stimuli and perform functional tasks without external guidance.
“Basically, these little nanobots become self-organized and self-aware,” said Igor Aronson, Huck Chair Professor of Biomedical Engineering, Chemistry and Mathematics at Penn State, explaining the plot of Crichton’s book. The novel inspired Aronson to study the emergence of collective movement among interacting, self-propelled agents. The research was recently published in Nature Communications.
Aronson and a team of physicists from LMU University, Munich, developed a new model to describe how biological or synthetic systems form complex structures equipped with minimal signal processing capabilities that enable the systems to respond to stimuli and perform functional tasks without performing external guidance. . The findings have implications in microrobotics and for any field involving functional, self-assembled materials formed from simple building blocks, Aronson said. For example, robotics engineers can create swarms of microrobots capable of performing complex tasks such as pollutant capture or threat detection.
“When we look at nature, we see that many living things rely on communication and teamwork because it increases their chances of survival,” Aronson said.
The computer model designed by researchers from Penn State and Ludwig-Maximillian University predicted that communication by small, self-propelled agents leads to intelligent collective behavior. The study showed that communication dramatically expands an individual unit’s ability to form complex functional states similar to living systems.
The team built their model to mimic the behavior of social amoebae, single-celled organisms that can form complex structures by communicating through chemical signals. They studied one phenomenon in particular. When food becomes scarce, the amoebae release a messenger chemical known as cyclic adenosine monophosphate (cAMP), which causes the amoebae to gather in one place and form a multicellular aggregate.
“The phenomenon is well known,” co-author Erwin Frey of Ludwig-Maximilians-Universität München said in a release. “Until now, however, no research group has investigated how information processing at a general level affects the assembly of systems of agents when individual agents—in our case, amoebae—are self-propelled.”
For decades, scientists have been striving for a better understanding of “active matter,” the biological or synthetic systems that harness energy stored in the environment, e.g. a nutrient, converted into mechanical movement and forming larger structures through self-organization. Taken individually, the material has no intelligence or functionality, but collectively the material is able to respond to its environment with a kind of nascent intelligence, Aronson explained. It’s an old concept with futuristic applications.
Aristotle articulated the theory of emergence some 2,370 years ago in his treatise “Metaphysics”. His language is commonly paraphrased as “the whole is greater than the sum of the parts.” In the not-so-distant future, Aronson says emerging systems research could lead to cell-sized nanobots that self-organize in the body to fight viruses or swarms of autonomous microrobots that can coordinate in complex formation without a pilot.
“We typically talk about artificial intelligence as a kind of sentient android with augmented thinking,” Aronson said. “What I’m working on is distributed artificial intelligence. Each element lacks any intelligence, but once they come together, they are capable of collective response and decision-making.”
There is currently a high demand for distributed artificial intelligence in the field of robotics, Aronson explained.
“If you’re designing a robot in the most cost-effective way possible, you don’t want to make it too complex,” he said. “We want to make small robots that are very simple, just a few transistors, that when they work together have the same functionality as a complex machine, but without the expensive, complicated machinery. This discovery will open new avenues for applications of active matter in nanoscience and robotics.
Aronson explained that from a practical point of view, distributed artificial intelligence can be used in any kind of substance that has microscopically dispersed particles in it. It could be deployed inside the body to deliver a drug to fight disease or to activate tiny electronic circuits in mass-produced microrobots.
“Despite its importance, the role of communication in the context of active matter remains largely unexplored,” the researchers wrote. “We identify the decision-making machinery of the individual active agents as the driving force for the collectively controlled self-organization of the system.”
Reference: Ziepke A, Maryshev I, Aranson IS, Frey E. Multi-scale organization in the communication of active matter. Nat Commun. 2022;13(1):6727. doi: 10.1038/s41467-022-34484-2
This article has been republished from the following material. Note: material may have been edited for length and content. For further information, please contact the cited source.