Olfactory search at high Reynolds number

Abstract

Locating the source of odor in a turbulent environment - a common behavior for living organisms - is non-trivial because of the random nature of mixing. Here we analyze the statistical physics aspects of the problem and propose an efficient strategy for olfactory search which can work in turbulent plumes. The algorithm combines the maximum likelihood inference of the source position with an active search. Our approach provides the theoretical basis for the design of olfactory robots and the quantitative tools for the analysis of the observed olfactory search behavior of living creatures (e.g. odor modulated optomotor anemotaxis of moth)

6 pages, 4 figures

Cited by in corpus (13)

• Intermittent search strategies
• Context-dependent interaction leads to emergent search behavior in social aggregates
• Sensing and decision-making in random search
• On the trajectories and performance of Infotaxis, an information-based greedy search algorithm
• Moth-inspired navigation algorithm in a turbulent odor plume from a pulsating source
• Optimal policies for Bayesian olfactory search in turbulent flows
• Collective olfactory search in a turbulent environment
• Starvation Dynamics of a Greedy Forager
• Many wrong models approach to localize an odor source in turbulence with static sensors
• Optimal trajectories for Bayesian olfactory search in turbulent flows: the low information limit and beyond
• Turbulent dispersion of breath by the wind
• Policy heterogeneity improves collective olfactory search in 3-D turbulence
• The flow generated by an active olfactory system of the red swamp crayfish