RT Journal Article
T1 Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots.
A1 Góngora González, Andrés
A1 González-Monroy, Javier
A1 Rahbar, Faezeh
A1 Ercolani, Chiara
A1 González-Jiménez, Antonio Javier
A1 Martinoli, Alcherio
K1 Robots autónomos
AB The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.
PB MDPI
YR 2023
FD 2023-06-07
LK https://hdl.handle.net/10630/27113
UL https://hdl.handle.net/10630/27113
LA eng
NO Gongora A, Monroy J, Rahbar F, Ercolani C, Gonzalez-Jimenez J, Martinoli A. Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots. Sensors. 2023; 23(12):5387. https://doi.org/10.3390/s23125387
NO Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026