Collective olfactory search in a turbulent environment

Abstract

Finding the distant source of an odor dispersed by a turbulent flow is a vital task for many organisms, either for foraging or for mating purposes. At the level of individual search, animals like moths have developed effective strategies to solve this very difficult navigation problem based on the noisy detection of odor concentration and wind velocity alone. When many individuals concurrently perform the same olfactory search task, without any centralized control, sharing information about the decisions made by the members of the group can potentially increase the performance. But how much of this information is actually valuable and exploitable for the collective task ? Here we show that, in a model of a swarm of agents inspired by moth behavior, there is an optimal way to blend the private information about odor and wind detections with the publicly available information about other agents' heading direction. At optimality, the time required for the first agent to reach the source is essentially the shortest flight time from the departure point to the target. Conversely, agents who discard public information are several fold slower and groups that do not put enough weight on private information perform even worse. Our results then suggest an efficient multi-agent olfactory search algorithm that could prove useful in robotics, for instance in the identification of sources of harmful volatile compounds.

References in corpus (9)

• Simulating Dynamical Features of Escape Panic
• Collective motion
• Cascades and transitions in turbulent flows
• Inverse cascade in two-dimensional turbulence: deviations from Gaussianity
• The Physics of the Vicsek Model
• Fractal clustering of inertial particles in random flows
• Olfactory search at high Reynolds number
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Cited by in corpus (5)

• Efficiency of random search with space-dependent diffusivity
• Many wrong models approach to localize an odor source in turbulence with static sensors
• Theory for Optimal Estimation and Control under Resource Limitations and Its Applications to Biological Information Processing and Decision-Making
• A model for cooperative scientific research inspired by the ant colony algorithm
• Policy heterogeneity improves collective olfactory search in 3-D turbulence