Prof. Romanczuk erforscht, wie Gruppen von Tieren oder Robotern gemeinsam Entscheidungen treffen und Informationen verarbeiten — von einzelnen Wahrnehmungen bis zum koordinierten Schwarmverhalten. Seine aktuelle Arbeit konzentriert sich auf kollektive Lernprozesse in Fischschwärmen und biologisch inspirierten Roboterschwärmen, um zu verstehen, wie dezentrale Systeme intelligent zusammenarbeiten können. Die Erkenntnisse sind relevant für die Entwicklung autonomer Robotersysteme, Schwarmrobotik und für Anwendungen, bei denen verteilte Agenten ohne zentrale Kontrolle koordiniert werden müssen — etwa in der Logistik, Überwachung oder Rettungsrobotik.
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Prof. Dr. Pawel Romanczuk
HU-FIS-Profil ↗EXC 2002: Science of Intelligence (SCIoI)
university
EXC 2002: Science of Intelligence (SCIoI)
university
Evolution von kollektiver Kognition als Reaktion auf neuartigen Selektionsdruck – Wie beeinflußt individuelle Adaptation kollektives Verhalten? (Auslauffinanzierung P52A)
other
Förderer: DFG Nachwuchsgruppe Zeitraum: 06/2016 - 04/2021 Projektleitung: Prof. Dr. Pawel Romanczuk
Förderer: DFG Exzellenzstrategie Cluster Projektleitung: Prof. Dr. Marcel Brass
Förderer: DFG Nachwuchsgruppe Zeitraum: 05/2019 - 08/2021 Projektleitung: Prof. Dr. Pawel Romanczuk
Proceedings of the National Academy of Sciences · DOI
Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a "crisis discipline" just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.
Physical Review Letters · DOI
Recent studies suggest that noncooperative behavior such as cannibalism may be a driving mechanism of collective motion. Motivated by these novel results we introduce a simple model of Brownian particles interacting by biologically motivated pursuit and escape interactions. We show the onset of collective motion for both interaction types and analyze their impact on the global dynamics. We demonstrate a strong dependence of experimentally accessible macroscopic observables on the relative strength of escape and pursuit and determine the scaling of the migration speed with model parameters.
Science Advances · DOI
Classical models of collective behavior often take a "bird's-eye perspective," assuming that individuals have access to social information that is not directly available (e.g., the behavior of individuals outside of their field of view). Despite the explanatory success of those models, it is now thought that a better understanding needs to incorporate the perception of the individual, i.e., how internal and external information are acquired and processed. In particular, vision has appeared to be a central feature to gather external information and influence the collective organization of the group. Here, we show that a vision-based model of collective behavior is sufficient to generate organized collective behavior in the absence of spatial representation and collision. Our work suggests a different approach for the development of purely vision-based autonomous swarm robotic systems and formulates a mathematical framework for exploration of perception-based interactions and how they differ from physical ones.