Towards Fast Plume Source Estimation with a Mobile Robot
The estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce t...
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doaj-8994d3068df7409da496392b4ba8b7692020-12-09T00:03:02ZengMDPI AGSensors1424-82202020-12-01207025702510.3390/s20247025Towards Fast Plume Source Estimation with a Mobile RobotHugo Magalhães0Rui Baptista1João Macedo2Lino Marques3 Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalThe estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume’s parameters after a reduced number of plume crossings.https://www.mdpi.com/1424-8220/20/24/7025mobile roboticsgas source localisationparticle filter |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hugo Magalhães Rui Baptista João Macedo Lino Marques |
spellingShingle |
Hugo Magalhães Rui Baptista João Macedo Lino Marques Towards Fast Plume Source Estimation with a Mobile Robot Sensors mobile robotics gas source localisation particle filter |
author_facet |
Hugo Magalhães Rui Baptista João Macedo Lino Marques |
author_sort |
Hugo Magalhães |
title |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_short |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_full |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_fullStr |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_full_unstemmed |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_sort |
towards fast plume source estimation with a mobile robot |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-12-01 |
description |
The estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume’s parameters after a reduced number of plume crossings. |
topic |
mobile robotics gas source localisation particle filter |
url |
https://www.mdpi.com/1424-8220/20/24/7025 |
work_keys_str_mv |
AT hugomagalhaes towardsfastplumesourceestimationwithamobilerobot AT ruibaptista towardsfastplumesourceestimationwithamobilerobot AT joaomacedo towardsfastplumesourceestimationwithamobilerobot AT linomarques towardsfastplumesourceestimationwithamobilerobot |
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