The phenomenon of self-organization occurs in many areas. In nature, for example, fish organize themselves to swim in well structured shoals, ants find shortest routes to food sources, and fireflies emit light flashes in synchrony. Other examples of self-organized behavior can be observed in economy, population dynamics, psychology, and brain theory, to give some examples.
In such systems, many entities — sometimes hundreds or thousands — follow a set of simple local rules that eventually lead to an emergent global behavior without the need for central coordination. Entities interact directly with each other and continuously react to changes in their local environment. Self-organizing systems are often failure-robust, scalable, and adaptive, thus being an important measure toward handling the increasing number of networked components in the Internet of things.
Despite the simple interaction between the components, designing technical systems that follow the same principles as nature poses an enormous challenge to engineers. Various researchers at the University of Klagenfurt and Lakeside Labs thus elaborate novel concepts, methods, and tools for engineering self-organizing systems.
“Evolution created natural self-organizing systems, which is why we opt for the same path,” explains Wilfried Elmenreich. His team works on evolving systems that employ an automated search systematically developing the fittest solution. To support such evolutionary design, the open-source tool FREVO has been developed and applied for experimental studies in social behavior, robot soccer, and area exploration.
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M. Schranz, M. Sende, M. Umlauft, and W. Elmenreich. Swarm robotic behaviors and current applications. Frontiers in Robotics and AI, 2020.
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