A Bayesian approach for gas source localization in large indoor environments

Prabowo, YA; Ranasinghe, R; Dissanayake, G; Riyanto, B; Yuliarto, B

A Bayesian approach for gas source localization in large indoor environments

Prabowo, YA Ranasinghe, R

Dissanayake, G

Riyanto, B Yuliarto, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, 00, pp. 4432-4437
Issue Date:: 2021-02-10

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Field	Value	Language
dc.contributor.author	Prabowo, YA
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680
dc.contributor.author	Riyanto, B
dc.contributor.author	Yuliarto, B
dc.date	2020-10-24
dc.date.accessioned	2021-09-23T06:02:02Z
dc.date.available	2021-09-23T06:02:02Z
dc.date.issued	2021-02-10
dc.identifier.citation	2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, 00, pp. 4432-4437
dc.identifier.isbn	978-1-7281-6212-6
dc.identifier.issn	2153-0858
dc.identifier.issn	2153-0866
dc.identifier.uri	http://hdl.handle.net/10453/150672
dc.description.abstract	The main contribution of this paper is a probabilistic estimator that assists a mobile robot to locate a gas source in an indoor environment. The scenario is that a robot equipped with a gas sensor enters a building after the gas is released due to a leak or explosion. The problem is discretized by dividing the environment into a set of regions and time into a set of time intervals. Likelihood functions describing the probability of obtaining a certain gas concentration measurement at a given location at a given time interval are assembled using data generated with GADEN, a three-dimensional gas dispersion simulator([1]). Given a measurement of the gas concentration is available, Bayes's rule is used to compute the joint probability density describing the location of the gas source and the time at which it started spreading. To illustrate the estimation process, a relatively simple motion planner that directs the robot towards the most likely gas source location using a cost function based on the marginal probability of the gas source location is used. The motion plan is periodically revised to reflect the latest posterior probability density. Simulation experiments in a large air-conditioned building with turbulence and wind are presented to demonstrate the effectiveness of the proposed technique.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
dc.relation.ispartof	IEEE/RSJ International Conference on Intelligent Robots and Systems
dc.relation.isbasedon	10.1109/iros45743.2020.9341747
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Bayesian approach for gas source localization in large indoor environments
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Las Vegas, NV, USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-09-23T06:02:00Z
pubs.finish-date	2021-01-24
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-10-24
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

The main contribution of this paper is a probabilistic estimator that assists a mobile robot to locate a gas source in an indoor environment. The scenario is that a robot equipped with a gas sensor enters a building after the gas is released due to a leak or explosion. The problem is discretized by dividing the environment into a set of regions and time into a set of time intervals. Likelihood functions describing the probability of obtaining a certain gas concentration measurement at a given location at a given time interval are assembled using data generated with GADEN, a three-dimensional gas dispersion simulator([1]). Given a measurement of the gas concentration is available, Bayes's rule is used to compute the joint probability density describing the location of the gas source and the time at which it started spreading. To illustrate the estimation process, a relatively simple motion planner that directs the robot towards the most likely gas source location using a cost function based on the marginal probability of the gas source location is used. The motion plan is periodically revised to reflect the latest posterior probability density. Simulation experiments in a large air-conditioned building with turbulence and wind are presented to demonstrate the effectiveness of the proposed technique.

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http://hdl.handle.net/10453/150672