Abstract
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.
3 More- Received 22 November 2019
- Revised 6 March 2020
- Accepted 3 June 2020
DOI:https://doi.org/10.1103/PhysRevE.102.012402
©2020 American Physical Society
Physics Subject Headings (PhySH)
synopsis
Follow the Crowd to Find a Smell
Published 7 July 2020
Simulations show that by trusting their neighbors and following their own “noses,” a swarm of fictitious organisms inspired by moths can quickly find a smell’s source in turbulent air.
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