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A Bayesian approach for gas source localization in large indoor environments
Prabowo Y.A.a, Ranasinghe R.b, Dissanayake G.b, Riyanto B.a, Yuliarto B.a
a Institut Teknologi Bandung, Indonesia
b University of Technology Sydney, Australia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Air-conditioned buildings,Bayesian approaches,Estimation process,Gas concentration measurement,Gas-source localization,Likelihood functions,Marginal probability,Posterior probability[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bayesian estimation,Gas source localization,Robot olfaction[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IROS45743.2020.9341747[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]