Learn to Trace Odors: Autonomous Odor Source Localization via Deep Learning Methods

Abstract

Autonomous odor source localization (OSL) has been viewed as a challenging task due to the nature of turbulent airflows and the resulting odor plume characteristics. Here we present an olfactory-based navigation algorithm via deep learning (DL) methods, which navigates a mobile robot to find an odor source without explicating specific search algorithms. Two types of deep neural networks (DNNs), namely traditional feedforward and convolutional neural networks (FNN and CNN), are proposed to generate robot velocity commands on x and y directions based on onboard sensor measurements. Training data is obtained by applying the traditional olfactory-based navigation algorithms, including moth-inspired and Bayesian-inference methods, in thousands of simulated OSL trials. After the supervised training, DNN models are validated in OSL tests with varying search conditions. Experiment results show that given the same training data, CNN is more effective than FNN, and by training with a fused data set, the proposed CNN achieves a comparable search performance with the Bayesian-inference method while requires less computational time.