INFERRING SOURCE DISTANCE FROM

Transcription

1 INFERRING SOURCE DISTANCE FROM THE FINESTRUCTURE OF GAS PLUMES USING METAL-OXIDE SENSORS Michael Schmuker School of Engineering and Informatics University of Sussex, Brighton, UK smoke plume image by william warby CC-BY 2.0 European Robotics Forum 2016 Cankarjev Dom, Ljubljana 23/3/2016

2 Why estimate the distance of a gas source? In biology: - Locating food sources, mating partners, predators In technology: beespotter.org/topics/genome/ istockphoto.com - Gas-based navigation, e.g. a robot locating the source of a gas leak - Environmental monitoring, e.g. pollution, wildfires, etc. ems on ose) odotech.com A. Lilienthal, Örebro University 2

3 Gas concentration vs. distance Naïve assumption: gas concentration indicates distance. Motivated by the concept of diffusion. - Gas concentration is highest at the source. - Diffusion dilutes the gas with increasing distance. Image source: Ursell, T. S. (2013). The Diffusion Equation: A Multidimensional Tutorial. Problems with the naïve assumption: - Concentration at distance d depends on concentration at the source. What if the source concentration is unknown? - Only valid in absence of air movement (i.e., wind) and turbulence. Not realistic. 3

4 The structure of gas plumes Gas plumes have a complex structure that can be characterised on three levels: Time-averaged: downwind parabola-like shape. Temporal mesoscale: Plume meandering Fine-scale: filamentous. Murlis, J., Elkington, J. S., and Cardé, R. T. (1992). Annu. Rev. Entomol. 37,

5 Intermittency vs. distance in a wind tunnel Wind tunnel experiments: d = 50 mm Most salient difference is the change in intermittency. Max. concentration is also affected. Average concentration would be influenced by both factors. d = 100 mm d = 200 mm d = 400 mm Justus, K. A., Murlis, J., Jones, C., and Cardé, R. T. (2002). Environ. Fluid Mech. 2, Figure 7. Representative traces from the axial center of the continuous plume using a minia- 5

6 Exploring the physical component of odour space Vergara et al 2013: Electronic gas sensors in a turbulent wind tunnel. - Figaro TGS26XX metal-oxide sensors Sensors at varying distance from the odour source. Public dataset, 18k 72-dimensional time-series recordings, approx. 120 GB. 6

7 Turbulent gas flow Concentration decreases with distance from source. But in order to predict distance, the concentration at the source must be known. In a turbulent environment, gas intermittency also depends on source distance. Can we exploit such spatiotemporal features of gas plumes for distance estimation? 7

8 Bandpass to obtain the interesting part of the signal Critical frequencies Hz, 0.5 Hz. Low band: gas flow on/off. High band: noise (mostly). Middle band:? 8

9 Spectral analysis Middle band varies the slower the farther from the source. Calculating relative power in the fourier spectrum: fcrit = 0.1 Hz Relative spectral content of high frequency predicts distance from source. Schmuker et al., submitted. 9

10 Bout-based distance prediction 3-stage filtering: low-pass -> differential -> leaky integration w/ exponential. Reveals bouts : portions of the signal with positive slope. Bout counts predict source distance. Schmuker et al., submitted. 10

11 Cross-wind statistics Variance of bout count indicates crosswind offset from plume centreline. 11

12 Potential strategy for gas-based robot navigation 12

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14 Students, Collaborators & Funding Students/Postdocs: Jan Sölter PhD, FU Berlin (now software dev, Berlin) Tara Dezhdar cand. PhD, FU Berlin Iulia Lungu cand. MSc BCCN Berlin Viktor Bahr Bachelor Bioinf, now TU Berlin Bahadir Kasap MSc, now cand. PhD Donders Inst., Nijmegen Benjamin Auffarth postdoc, now FORTH, Heraklion Stephan Gabler MSc BCCN, now software dev, SF Bay Area Marcus Schroeder MSc Bioinf, software dev, Berlin Funding: Collaborators: Gas sensors & bioinspired machine learning Alan Diamond, Thomas Nowotny University of Sussex, Brighton, UK Ramon Huerta BioCircuits Institute, UC San Diego, USA Mouse olfactory bulb Jan Schumacher, HartwigSpors MPI for Biophysics, Frankfurt (now University Hospital Giessen, Germany) Neuromorphic Hardware Thomas Pfeil, Karlheinz Meier Kirchhoff-Institute for Physics, Uni Heidelberg, Germany SPP 1392: Integrative Analysis of Olfaction Marie Curie IEF Insect olfactory physiology Silke Sachse MPI for Chemical Ecology, Jena, Germany Neuronal coding of pain Gary Lewin, Rabih Moshourab Max-Delbrück-Centre for Molecular Medicine, Berlin 14