RT Journal Article
T1 VGR Dataset: A CFD-based Gas Dispersion Dataset for Mobile Robotic Olfaction
A1 Ojeda, Pepe
A1 González-Monroy, Javier
A1 González-Jiménez, Antonio Javier
K1 Ingeniería de sistemas
AB There are many potential applications for an autonomous robotic agent capable of sensing gases in the environment, from locating leaks in pipes to monitoring air quality. However, the current state of the art in the field of robotic olfaction is not mature enough for most real-world applications. Due to the complexity of gas dispersion phenomena and the limitations of sensors, a great deal of research into the development of techniques and algorithms remains necessary. A very important part of this research is thorough experimentation, but carrying out robotic olfaction experiments is far from trivial. Real world experiments are usually limited to very simplified, wind-tunnel-like environments, as it is impossible to closely monitor or control the airflow in more complex scenarios. For this reason, simulation with CFD offers the most plausible alternative, allowing researchers to study the behavior of their algorithms in more challenging and complex situations. This work presents a CFD-based gas dispersion dataset composed of 120 cases generated under variable environmental conditions, taking place in 30 realistic and detailed models of real houses. All the data is made available in multiple formats, and is directly accessible through ROS, to permit easy integration with other robotic tools.
PB Springer Nature
YR 2023
FD 2023-11-16
LK https://hdl.handle.net/10630/28912
UL https://hdl.handle.net/10630/28912
LA spa
NO Ojeda, P., Monroy, J. & Gonzalez-Jimenez, J. VGR Dataset: A CFD-based Gas Dispersion Dataset for Mobile Robotic Olfaction. J Intell Robot Syst 109, 75 (2023). https://doi.org/10.1007/s10846-023-02012-z
NO Funding for open Access charge: Universidad de Málaga / CBUA.This work was funded by the Andalucian Government (Junta de Andalucía), under the HOUNDBOT project (P20_01302) and the grant for the formation of pre-doctoral researchers (24653).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026