CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization

Abstract

Locating the source of harmful, flammable, or polluting gas leaks is an important task in many practical scenarios. A recently proposed localization approach is to use a mobile robot equipped with a chemical sensor. The localization algorithm guides the movement of the robot based on the previous observations, with the objective of reaching the source as quickly as possible. In this paper, we propose an approach where the robot policy is represented by a neural network combining convolutional and LSTM layers. The approach relies on a gas dispersion model that takes into account obstacles, wind direction, and molecular movement. We found that the trained model provides a 47.34% higher success rate in finding the gas source than an existing greedy approach on test cases with unseen gas plumes and random obstacles.