Researchers have harnessed the self-organization skills required to reap the benefits of natural swarms for robotic applications in artificial intelligence, computing, search and rescue, and more.
They describe their method in "Global-to-Local Design for Self-Organized Task Allocation in Swarms," published in the journal Intelligent Computing.
"The behavior of the swarm [of robots] is not a one-to-one map with simple rules executed by individual robots, but rather results from the complex interactions of many robots executing the same set of rules," says Marco Dorigo, professor in the artificial intelligence laboratory of the Université Libre de Bruxelles, Belgium.
The researchers propose a global-to-local design approach. "Our key idea is to compose a heterogenous swarm using groups of behaviorally different agents such that the resulting swarm behavior approximates a user input representing the behavior of the entire swarm," Dorigo says.
From Intelligent Computing
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