Scientists find curvy answer to harnessing “swarm intelligence”

Birds flock in order to forage and move more efficiently. Fish school to avoid predators. And bees swarm to reproduce. Recent advances in artificial intelligence have sought to mimic these natural behaviors as a way to potentially improve search-and-rescue operations or to identify areas of wildfire spread over vast areas—largely through coordinated drone or robotic movements. However, developing a means to control and utilize this type of AI—or “swarm intelligence”—has proved challenging. 

In a newly published paper, an international team of scientists describes a framework designed to advance swarm intelligence—by controlling flocking and swarming in ways that are akin to what occurs in nature.

“One of the great challenges of designing robotic swarms is finding a decentralized control mechanism,” explains Matan Yah Ben Zion, an assistant professor at the Donders Center for Cognition at the Netherlands’ Radboud University and one of the authors of the paper, which appears in the journal Proceedings of the National Academy of Sciences. “Fish, bees, and birds do this very well—they form magnificent structures and function without a singular leader or a directive. By contrast, synthetic swarms are nowhere near as agile—and controlling them for large-scale purposes is not yet possible.”

The research team, which included NYU scientists Mathias Casiulis and Stefano Martiniani, addressed these challenges by developing geometric design rules for the clustering of self-propelled particles. These rules are modeled using natural computation—similar to the “positive” or “negative” charges in protons and electrons that are foundational to the formation of matter. 

Under these rules, active particles moving in response to external force have an intrinsic property that causes them to curve—a quantity the researchers call “curvity.” 

“This curvature drives the collective behavior of the swarm, which points to a means to potentially control whether the swarm flocks, flows, or clusters,” explains NYU’s Martiniani, an assistant professor of physics, chemistry, and mathematics. 

Their conclusion was supported by a series of experiments in which the scientists showed that the curvature-based criterion controls robot-pair attraction and naturally extends to thousands of robots. Each robot was treated as having a positive or negative curvity, and similar to electric charge, this curvity controls the robots’ mutual interactions. 

“This charge-like quantity, which we call ‘curvity,’ can take positive or negative values and can be directly encoded into the mechanical structure of the robot,” explains Ben Zion. “As with particle charges, the value of the curvity determines how robots become attracted to one another in order to cluster or deflect from one another in order to flock.” 

Ben Zion, who as an NYU student previously developed microscopic swimmers added: “Finding a design rule of geometric nature, such as curvature, makes it applicable to industrial or delivery robots or to cellular-sized microscopic robots that have potential to improve drug delivery and other medical treatments.” 

“The best part is that these rules are based on elementary mechanics, making their implementation in a physical robot straightforward,” adds Casiulis, a postdoctoral researcher at New York University’s Center for Soft Matter Research and NYU’s Simons Center for Computational Physical Chemistry. “More broadly, this work transforms the challenge of controlling swarms into an exercise in material science, offering a simple design rule to inform future swarm engineering.”

The study’s other authors were Tel Aviv University researchers Eden Arbel, Yoav Lahini, and Naomi Oppenheimer and Radboud University researcher Charlotte van Waes.

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