Gas Source Localization in a Built Environment


The deployment of robots for Gas Source Localization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Robotic Olfaction is an active and challenging research topic due to the chaotic and intermittent nature of the gas dispersion phenomenon. As a result, gas sensing using mobile robots focuses primarily on simplified scenarios, with a steady wind applied to an obstacle-free environment,  with a current trend toward tackling more complex environments. The increased complexity of the experimental testbeds, introduced by the presence of obstacles and low (or absence of) wind intensity, adds additional challenges in developing a faithful gas dispersion simulation, gathering reliable gas sensing as well as designing gas sensing robot systems.

This project focuses on the integration of a probabilistic framework that takes into account the uncertainties of the gas dispersion phenomenon, along with the use of information gathering-based navigation strategies. In the same time, a physically distributed solution consisting of cooperative multiple sensing assets characterized by different degrees of mobility will also be explored, as a potential way to gather data in parallel and share more information about the underlying gas dispersion.

Team and Collaborators

Research Period and Sponsors

This project started in October 2018.

SNSF is sponsoring this project for a duration of 4 years.

  • DISAL-SP178: Michael Freeman, Sensor Network Development for Gas Source Localization
  • DISAL-SP181: Jonathan Henry, Particle Filters for Gas Source Localization in Cluttered Environments
  • DISAL-MP52: Mael Feurgard, Control a fleet of UAVs to explore a fire plume
  • DISAL-SP172: Karim Zahra, Gas Source Localization Under Realistic Environmental Conditions with Gas Sensing Robots
  • DISAL-SP168: Malika In-Albon, Algorithms for Gas Source Localization using MOX Sensors
  • DISAL-IP39: Wanting Jin, Towards 3D Gas Source Localization in Realistic Indoor Environments using Micro Aerial Vehicles


Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots

W. Jin; F. Rahbar; C. Ercolani; A. Martinoli 

2023-06-03. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, May 29 – June 2, 2023. DOI : 10.1109/ICRA48891.2023.10160816.