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Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0002-5973-7424
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0002-0804-8637
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2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE conference proceedings, 2017, article id 17013581Conference paper, Published paper (Refereed)
Abstract [en]

Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2017. article id 17013581
National Category
Computer Sciences Robotics
Identifiers
URN: urn:nbn:se:oru:diva-60633DOI: 10.1109/ISOEN.2017.7968874ISBN: 978-1-5090-2392-9 (electronic)ISBN: 978-1-5090-2393-6 (print)OAI: oai:DiVA.org:oru-60633DiVA, id: diva2:1138648
Conference
2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal, QC, Canada
Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2024-01-03Bibliographically approved
In thesis
1. Efficient Remote Gas Inspection with an Autonomous Mobile Robot
Open this publication in new window or tab >>Efficient Remote Gas Inspection with an Autonomous Mobile Robot
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions.

This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments.

In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping.

A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleoperated by human experts. In six experimental trials, the autonomous system created better gas maps, located more gas sources correctly, and provided better sensing coverage with fewer sensing positions than human experts.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2020. p. 78
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 88
Keywords
environmental monitoring, measurement planning, remote gas sensing, mobile robot olfaction, service robots
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-87393 (URN)978-91-7529-344-8 (ISBN)
Public defence
2020-12-18, Örebro universitet, Forumhuset, Hörsal F, Fakultetsgatan 1, Örebro, 14:00 (English)
Opponent
Supervisors
Available from: 2020-11-16 Created: 2020-11-16 Last updated: 2024-01-03Bibliographically approved

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Fan, HanArain, Muhammad AsifHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim J.

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