An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search

  • Authors:

  • Pawel Pyk;Sergi Bermúdez I Badia;Ulysses Bernardet;Philipp Knüsel;Mikael Carlsson;Jing Gu;Eric Chanie;Bill S. Hansson;Tim C. Pearce;Paul F. J. Verschure

  • Affiliations:

  • Institute of Neuroinformatics, University and ETH Zurich, Zurich, Switzerland CH-8057;Institute of Neuroinformatics, University and ETH Zurich, Zurich, Switzerland CH-8057;Institute of Neuroinformatics, University and ETH Zurich, Zurich, Switzerland CH-8057;Institute of Neuroinformatics, University and ETH Zurich, Zurich, Switzerland CH-8057;Department of Crop Science, Swedish University of Agricultural Sciences, Alnarp, Sweden SE-230 53;NeuroLab, Centre for Bioengineering, Department of Engineering, University of Leicester, Leicester, UK LE1 7RH;Alpha MOS, Toulouse, France 31400;Department of Crop Science, Swedish University of Agricultural Sciences, Alnarp, Sweden SE-230 53;NeuroLab, Centre for Bioengineering, Department of Engineering, University of Leicester, Leicester, UK LE1 7RH;Aff1 Aff2

  • Venue:
  • Autonomous Robots
  • Year:
  • 2006

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Abstract

Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robots.