Collective olfactory search in a turbulent environment

Durve, Mihir; Piro, Lorenzo; Cencini, Massimo; Biferale, Luca; Celani, Antonio

doi:10.1103/PhysRevE.102.012402

Lokale TagsFreigabegeschichteDetailsÜbersicht

Collective olfactory search in a turbulent environment

Durve, M., Piro, L., Cencini, M., Biferale, L., & Celani, A. (2020). Collective olfactory search in a turbulent environment. Physical Review E, 102: 012402. doi:10.1103/PhysRevE.102.012402.

Item is Freigegeben

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:

Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0007-2EA8-9 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0009-ECA8-0

Genre: Zeitschriftenartikel

ausblenden:

Urheber:
Durve, Mihir, Autor
Piro, Lorenzo¹, Autor
Cencini, Massimo, Autor
Biferale, Luca, Autor
Celani, Antonio, Autor

Affiliations:
1Department of Living Matter Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, DE, ou_2570692

Inhalt

einblenden:

ausblenden:

Schlagwörter: -

Zusammenfassung: Finding the source of an odor dispersed by a turbulent flow is a vital task for many organisms. When many individuals concurrently perform the same olfactory search task, sharing information about other members' decisions can potentially boost the performance. But how much of this information is actually exploitable for the collective task? Here we show, in a model of a swarm of agents inspired by moth behavior, that there is an optimal way to blend the private information about odor and wind detections with the public information about other agents' heading direction. Our results suggest an efficient multiagent olfactory search algorithm that could prove useful in robotics, e.g., in the identification of sources of harmful volatile compounds.