![\"\"](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2025\/09\/1754082956011.jpg\" "\\"Schooling")
The robot design used in the study. <\/p>\nA new framework has been designed to push forward swarm intelligence, the branch of AI that mimics the group behaviors of birds, fish, and bees.<\/p>\n
The coordinated movement of robots could improve search-and-rescue operations and wildfire detection.<\/p>\n
The collective intelligence found in nature is a wonder of efficiency and coordination. Birds flock to forage. Fish school as a way to avoid predators. Bees use swarming as their method of reproduction. <\/p>\n
However, replicating this self-organizing behavior in artificial swarms has been a major challenge for researchers.<\/p>\n
\u201cOne of the great challenges of designing robotic swarms is finding a decentralized control mechanism,\u201d said Matan Yah Ben Zion, an assistant professor at Radboud University\u2019s Donders Center for Cognition, in a press release Monday from New York University (NYU).<\/p>\n
\u201cFish, bees, and birds do this very well\u2014they form magnificent structures and function without a singular leader or a directive. By contrast, synthetic swarms are nowhere near as agile\u2014and controlling them for large-scale purposes is not yet possible,\u201d added Ben Zion, the study author.<\/p>\n
The robot design used in the study. <\/p>\n
A simple rule for complex behavior<\/p>\n
The study focuses on a central challenge for robotic swarms: establishing a decentralized control mechanism. <\/p>\n
This is a way to ensure robots can work together effectively as a group, similar to a flock of birds or a school of fish, even without a single guiding authority.<\/p>\n
The researchers developed a set of geometric design rules for controlling swarms<\/a> to overcome this issue.\u00a0<\/p>\n These new rules are based on natural computation, like protons and electrons\u2019 positive and negative charges.\u00a0<\/p>\n A new quantity called \u201ccurvity\u201d was introduced to the model. Curvity is an intrinsic charge-like quality that causes a robot to curve in response to an external force.<\/p>\n According to the new framework, each robot is assigned a positive or negative curvity value to control the way it interacts with its fellow robots.<\/p>\n \u201cThis curvature drives the collective behavior of the swarm, which points to a means to potentially control whether the swarm flocks, flows, or clusters,\u201d said<\/a> Stefano Martiniani, assistant professor at New York University.<\/p>\n Applications in drug delivery <\/p>\n In a series of experiments, the researchers successfully demonstrated this new framework. \u00a0It showcased that their curvature-based criterion controls how pairs of robots<\/a> are attracted to one another.\u00a0<\/p>\n Moreover, this principle naturally scales up to control the movements of thousands of robots.\u00a0Researchers discovered that it can be directly embedded into a robot\u2019s mechanical design.\u00a0<\/p>\n Interestingly, this geometric rule could be applied to large industrial or delivery robots<\/a> and microscopic robots designed for medical treatments like drug delivery.<\/p>\n These new rules for swarm control are based on simple, elementary mechanics, making them easy to implement in a physical robot.\u00a0<\/p>\n Furthermore, the new strategy could transform the challenge of controlling swarms from a complex programming problem into an issue of material science.<\/p>\n The development of robotic swarms is an evolving field with several recent advancements. <\/p>\n In April,\u00a0H2 Clipper\u00a0secured a patent for using robotic swarms in large-scale aerospace manufacturing. <\/a><\/p>\n