Data-driven olfactory search in a turbulent flow
ORAL
Abstract
Tracking the source of an odor which is advected by a turbulent flow is an important behavior for many flying insects and other animals. This olfactory search problem is rendered especially difficult by the intermittency intrinsic to turbulence, and it requires complex search strategies which properly leverage infrequent odor detections. In this work, we perform direct numerical simulations (DNS) of tracer particles emitted from a points source in a turbulent flow with a mean wind. We study the concentration statistics of the tracer data and use the data to extract model-based policies for search. We compare the empirical performance of near optimal policies (in the sense of partially observable Markov decision processes) to that of several heuristics.
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Presenters
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Robin Heinonen
University of Rome
Authors
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Robin Heinonen
University of Rome
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Fabio Bonaccorso
University of Roma Tor Vergata & INFN, Department of Physics and INFN, University of Rome "Tor Vergata", Via della Ricerca Scientifica 1, 00133, Rome, Italy, Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, University of Rome
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Luca Biferale
University of Roma Tor Vergata & INFN, University of Rome Tor Vergata, Department of Physics and INFN, University of Rome "Tor Vergata", Via della Ricerca Scientifica 1, 00133, Rome, Italy, University of Rome
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Antonio Celani
ICTP, Trieste, The Abdus Salam International Centre for Theoretical Physics
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Massimo Vergassola
Ecole Normale Superieure