Robotic odor source localization via adaptive bio-inspired navigation using fuzzy inference methods

Abstract

Robotic odor source localization (OSL) has been viewed as a challenging task due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to solving an OSL problem is designing an effective olfactory-based navigation algorithm, which guides a plume-tracing robot to find the odor source via tracing emitted plumes. Inspired by the mate-seeking behaviors of male moths, this article presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source in an unknown environment. Unlike traditional bio-inspired algorithms, which use fixed parameters to formulate robot search trajectories, we design a fuzzy controller to perceive the environment and adjust trajectory parameters based on the current search situation. Therefore, the robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. Simulation and on-vehicle results show that compared to two classical olfactory-based navigation algorithms, the proposed algorithm is more efficient and outperforms them in terms of the averaged search time and success rate.