A pheromone-guided mobile robot that behaves like a silkworm moth with living antennae as pheromone sensors

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 artificial 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 behavior; 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. 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.