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A Pheromone-Guided Mobile Robot that Behaves like a Silkworm Moth with Living Antennae as Pheromone Sensors
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- Yoshihiko Kuwana
- Department of Mechano-Informatics The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113, Japan
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- Isao Shimoyama
- Department of Mechano-Informatics The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113, Japan
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Description
<jats:p> In this paper, the pheromone-oriented behavior of moths will be demonstrated by synthesis with biosensors and a small mobile robot that is controlled by recurrent neural networks. Since antennae on a silkworm moth are very sensitive as compared to conventional arti ficial gas sensors, they can be used as living gas sensors that detect pheromone molecules. A simple recurrent artificial neural network was used to control pheromone-tracing behavior in the manner of a living male silkworm moth. This neural network generates mothlike behavior while interacting with the environment. The turning behav ior, in particular, is a suitable tactic for small intelligence when a robot misses pheromone molecules. Our neural network is so simple that it can be very easily used as the controlling devices for micro- robots, which have a small amount of space for intelligence. Our robot is a hybrid system that combines living organisms and artificial machines, and is therefore a new type of robot. </jats:p><jats:p> This approach has the advantage of a real-world experiment with biosensors instead of computer simulation. The difference between the real world and the simulation conditions yields a discrepancy in results. An additional discrepancy is generated by the sensor model. Therefore, real-world experiments with living antennae may provide a fascinating interface between computer simulation and neuroethology. </jats:p>
Journal
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- The International Journal of Robotics Research
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The International Journal of Robotics Research 17 (9), 924-933, 1998-09
SAGE Publications
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Details 詳細情報について
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- CRID
- 1361699996324985728
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- NII Article ID
- 30026409631
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- ISSN
- 17413176
- 02783649
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- Data Source
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- Crossref
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