Evolutionary robotics approach to odor source localization

An evolutionary robotics approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated turbulent odor plumes. In the experiments, the simulated robot is equipped with a single chemical sensor and a wind direction sensor. Two main contributions are made. First, it is shown that a single CTRNN is able to successfully localize an odor source under turbulent conditions. It performs all three phases of the task: (i) finding the odor plume, (ii) moving toward the odor source, and (iii) identifying the odor source. Second, the analysis of the evolved behaviors reveals two novel odor source localization strategies. These strategies are successfully re-implemented as finite state machines, validating the insights from the analysis of the neural controllers.