An Implementation of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for Odor Source Localization

Abstract

In this paper, we investigate the viability of implementing machine learning (ML) algorithms to solve the odor source localization (OSL) problem. The primary objective is to obtain an ML model that guides and navigates a mobile robot to find an odor source without explicating searching algorithms. To achieve this goal, the model of an adaptive neuro-fuzzy inference system (ANFIS) is employed to generate the olfactory-based navigation strategy. To train the ANFIS model, multiple training data sets are acquired by applying two traditional olfactory-based navigation methods, namely moth-inspired and Bayesian-inference methods, in hundreds of simulated OSL tests with different environments. After training with the hybrid-learning algorithm, the ANFIS model is validated in multiple OSL tests with varying searching conditions. Experiment results show that the ANFIS model can imitate other olfactory-based navigation methods and correctly locate the odor source. Besides, by training it with the fused training data set, the ANFIS model is better than two traditional navigation methods in terms of the averaged searching time.