Gas source localization using Dueling Deep Q-Network with an olfactory quadruped robot

Abstract

Gas source localization is one of the most common applications of a gas-sensitive mobile robot. However, most of the existing work focuses on rule-based algorithms for wheeled robots, which are difficult to apply in complex terrain with obstacles. In this article, we propose an olfactory quadruped robot to perform the gas source localization task using the Dueling Deep Q-Network (Dueling DQN) algorithm. For training, we designed a set of environments and imported gas dispersion data from computational fluid dynamics (CFD) software to construct a simulator. The olfactory quadruped robot was trained in this simulator using the Dueling DQN algorithm to learn how to find the gas source. The trained neural network was then deployed on the olfactory quadruped robot. Our method has been tested in both simulation and real environments. The olfactory quadruped robot can traverse rugged terrain in real experiments and efficiently find gas sources, demonstrating that our method is highly robust and has important practical value.