Gas Distribution Modeling

Gas distribution modeling (GDM) is the process of deriving a truthful representation of how gases are dispersed in an environment from a set of spatially and temporally distributed measurements of relevant variables.

Typically, gas measurements are gathered by a network of stationary sensors placed at strategic positions, which send the concentration values to a central station for data processing. 

Example of a GDM generated from a stationary sensor network. (left) Ground-truth gas concentration profile with detail of the measurement locations as red points. (right) Estimated gas distribution from the sensor measurements.

Nevertheless, GDM constitutes an ideal application area for mobile robots since they can provide a higher (and adaptive) resolution of the distribution model than a stationary network, while still offering the required accurate localization of each measurement. Installing sensors on mobile robots allows the creation of gas distribution models on the-fly, thus the possibility of making decisions upon such a model, as for example, what locations to explore next.

Example of a GDM generated by a mobile robot equipped with an electronic nose (e-nose). (left) Ground-truth gas concentration profile with detail of the robot path and measurement locations as red points. (right) Estimated gas distribution from the sensor measurements.

Building GDM with a mobile robot is a challenging task, mainly because in many realistic scenarios gas is dispersed by turbulent advection, which creates packets of gas that follow chaotic trajectories. This means that the gas distribution is, in general, not static but is continuously changing instead. Despite these problems, GDM has important applications in industry, science, and every-day life. Mobile robots equipped with gas sensors are deployed, for example, for pollution monitoring in public areas, surveillance of industrial facilities producing harmful gases, or for the inspection of contaminated areas within rescue missions.

Some results

The next video shows an example of GDM with a mobile robot. In this case, we employ a probabilistic approach to GDM based on the Kalman filter (see publications section below).


Pojects related to this topic include:


We collaborate with some researchers and institutions, covering from research stays to publications and talks. The most recent collaborations are listed bellow:

  • Centre for Applied Autonomous Sensor Systems (AASS). In addition to multiple stays in their research lab at Örebro University (Sweeden), we actively collaborate with the Assoc. Prof. Achim Lilienthal and Dr. Marco Trincavelli from the Mobile Robotics and Olfaction Lab (MR&O), in the development of algorithms for gas sensing calibration, gas distribution mapping, and  signal conditioning. Several papers and articles (listed in the Publication section) are result of such collaboration. 


Please refer to the following articles for further details:

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