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.
Related Student Projects and Internships
- DISAL-MP55: Hugo Miranda Queiros, A Probabilistic Error Model of ArUco Features
- DISAL-SP191: Wassim Maj and Yanik Haas, Development for Environmentally-Relevant Educational Scenarios with Arduino Kits
- DISAL-SP189: Théodore Maradan, Incorporation of Physics Informed Neural Networks (PINNs) for Gas Source Localization Task
- DISAL-MP53: Agatha Duranceau, Towards Robust Gas Source Localization in Built Environments: Algorithm Design and Validation
- DISAL-SP182: Raphaël Dousson, Miniature Wind Sensing Module for Robotic Application: Design and Validation
- DISAL-SU37: Mahdi Atallah, Gas Source Location Classification in Built Environment with a Sensor Network
- 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
Publication
Please note that the publication lists from Infoscience integrated into the EPFL website, lab or people pages are frozen following the launch of the new version of platform. The owners of these pages are invited to recreate their publication list from Infoscience. For any assistance, please consult the Infoscience help or contact support.