Searching an odor source is a challenging task for a distributed autonomous mobile robot due to the complexity of odor formation in the air. Because the shapes of odor plumes highly depend on the environmental conditions including dynamical changes of wind direction, a robust performance of searching is highly demanded. In this paper, we introduced an olfactory active sampling device that was equipped electronic fan to generate air current and suction olfactory information to the sensors. Our main contribution was proposing and evaluating adaptive sampling strategies for appropriate odor-searching behavior in real environments by using the device. The active sampling device was designed after observations of the wing-flapping-effect of a male silkworm moth, which corresponding to sniffing of mammals.The device was composed of an electronic fan, two (left and right) gas sensors embedded in air ducts, and was attached to a mobile robot. Each duct had a flange so as to take the air in front of a robot effectively, and the electric fan attached on the end of the duct excluded odors incoming from the rear. We settled different environmental conditions in which the fluctuation of the air flow was changed. We conducted experiments to verify the feasibility of the adaptive sampling system. As a result, under most of the environments, the robot with active sampling showed better performance than that without active sampling. However, the result showed that optimal active sampling has environmental dependency. To realize more adaptive system, we suggested that it is necessary to change the activeness of inspiration depending on the situation.
In this study, to improve performance of autonomous odor source search robot, we remarked artificial olfactory system which has smell intake function similar to animal s sniffing. The practical use of the robot is expected in various fields such as rescue and the poisonous gas counterterrorism. The olfactory system can exchange the intake speed, placement of gas sensors and shape of itself so that we can inspect a variety of intake strategies. To evaluate performance of intake strategies, we carried out odor source searching experiment by using the real robot in plural air flow environments. Under most environment, robot using air intake reached to odor source with higher probability than not using intake. On the other hand, higher intake speed does not achieved higher performance.
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