Learning from the Moth: A Comparative Study of Robot‐Based Odor Source Localization Strategies

The odor search strategies of the moth have been researched since many decades. Many behavioral studies have described the behavior under well controlled conditions, making predictions on what the underlying mechanisms might be. However, it is almost impossible to asses these mechanisms directly since sensory and behavioral data on a freely behaving moth are very hard to obtain. Therefore, we propose a comparative study were the behavior of a robot is analyzed when controlled by a number of odor source localization models. Our results show that a system making use of stereo odor information outperforms some well‐established chemical search models.

[1]  J. Hildebrand,et al.  Local interneurons and information processing in the olfactory glomeruli of the moth Manduca sexta , 1993, Journal of Comparative Physiology A.

[2]  Thomas C. Baker,et al.  A non‐anemotactic mechanism used in pheromone source location by flying moths , 1983 .

[3]  T. Baker,et al.  Pheromone Source Location by Flying Moths: A Supplementary Non-Anemotactic Mechanism , 1982, Science.

[4]  Ryohei Kanzaki,et al.  Neural basis of odor-source searching behavior in insect brain systems evaluated with a mobile robot. , 2004, Chemical senses.

[5]  S. Alexandersen,et al.  Purification and characterization of the major nonstructural protein (NS-1) of Aleutian mink disease parvovirus , 1995, Journal of virology.

[6]  Paul F. M. J. Verschure,et al.  IQR: a distributed system for real-time real-world neuronal simulation , 2002, Neurocomputing.

[7]  J. Kennedy,et al.  Pheromone-Regulated Anemotaxis in Flying Moths , 1974, Science.

[8]  Tom Duckett,et al.  Experimental analysis of gas-sensitive Braitenberg vehicles , 2004, Adv. Robotics.

[9]  T. Baker,et al.  Reiterative responses to single strands of odor promote sustained upwind flight and odor source location by moths. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[10]  E. A. Arbas,et al.  Physiology and morphology of projection neurons in the antennal lobe of the male mothManduca sexta , 1989, Journal of Comparative Physiology A.

[11]  Ryohei Kanzaki,et al.  Synthesis of the pheromone-oriented behavior of silkworm moths by a mobile robot with moth antennae as pheromone sensors , 1999 .

[12]  J. Hildebrand,et al.  Functionally distinct subdivisions of the macroglomerular complex in the antennal lobe of the male sphinx moth Manduca sexta , 1991, The Journal of comparative neurology.

[13]  Mark A. Willis,et al.  Effects of intermittent and continuous pheromone stimulation on the flight behaviour of the oriental fruit moth, Grapholita molesta , 1984 .

[14]  Effect of the fine‐scale structure of pheromone plumes: pulse frequency modulates activation and upwind flight of almond moth males , 1995 .

[15]  Roger D. Quinn,et al.  A robotic platform for testing moth-inspired plume tracking strategies , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[16]  Jing Gu,et al.  An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search , 2006, Auton. Robots.

[17]  B. Hansson,et al.  Central processing of pulsed pheromone signals by antennal lobe neurons in the male moth Agrotis segetum. , 1999, Journal of neurophysiology.

[18]  Steven Schofield,et al.  Flight behaviour of Cadra cautella males in rapidly pulsed pheromone plumes , 2002 .

[19]  Ryohei Kanzaki,et al.  Neural control mechanisms of the pheromone‐triggered programmed behavior in male silkmoths revealed by double‐labeling of descending interneurons and a motor neuron , 2005, The Journal of comparative neurology.

[20]  Paul F. M. J. Verschure,et al.  A Biologically Based Chemo-Sensing UAV for Humanitarian Demining , 2007 .

[21]  V. Braitenberg.  Vehicles, Experiments in Synthetic Psychology , 1984 .

[22]  Boris I. Shraiman,et al.  Olfactory search at high Reynolds number , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Paul F. M. J. Verschure,et al.  A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance , 2007, Int. J. Robotics Res..