GDM-Net++: Multi-robot 2D and 3D Gas Distribution Mapping Via Deep Q-Learning and Gaussian Process Regression

Abstract

Gas distribution mapping (GDM) refers to the task of mapping the gas concentrations of an airborne chemical over a region of interest. A mobile robot equipped with a gas sensor can be used potentially autonomously to build such a distribution map. However, modern-day robots might not have enough battery power to cover the entire area of interest. Therefore, a group of n such collaborative mobile robots can be used for this purpose. The goal of the robots is to sample concentrations from a fraction of locations and infer the gas intensities in the rest of the area using a supervised machine learning technique, namely the Gaussian Process (GP). To this end, we propose a novel multi-robot gas distribution mapping framework, named GDM-Net++, which works in both 2D and 3D settings. Our proposed framework first divides the environment into n unique regions using Voronoi partitioning. Next, we employ a multi-agent deep Q-learning framework for the robots to learn a joint policy. As GP is a compute-intensive process, during testing, the learned policy is applied without re-training the GP model. The experiments are performed in simulation using Python on six types of Gaussian plumes to validate our proposed technique. Compared to two baselines – greedy and random walk, GDM-Net++ performs by 278% and 852% better in terms of earned rewards, while outperforming them by 34% and 155%, respectively, in terms of the precision of gas distribution modeling across unseen 2D test cases. Our approach can also gracefully handle 2D GDM scenarios where the distribution is consistently affected by wind.