Phenotaxis: Localization of The Source of Phenomena Using Mobile Searchers

Abstract

Over the years, the use of robotic searchers in times of disaster and dangerous incidents, such as toxic gas leakage, has become of higher significance. Using robot searchers instead of humans or animals in such dangerous situations significantly reduces risks. Several algorithms have been developed for robot searchers to search for the source of incident by following the emitted odor via Odor Source Localization (OSL). In this thesis, we investigate the performance of several gradient-based and bio-inspired OSL algorithms in a turbulent environment, where the performance is greatly impacted due to reliance on the odor concentration. Additionally, we evaluate the performance of multiple cooperating searchers performing OSL through communicating via an error channel and observe how the error can affect the search, showing that high error in the channel can mislead the search. Additionally, a Reinforcement Learning (RL)-based approach to OSL is proposed, which shows an improvement in the search efficiency. Further, the potential of a searcher that is assisted by aWireless Sensor Network (WSN) is studied, where two cooperation strategies between the searcher and the WSN are proposed, showing potential in improving the performance of a single searcher up to the performance of multiple searchers.