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Statistical Gas Distribution Modelling for Mobile Robot Applications

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Statistical Gas Distribution Modelling for Mobile Robot Applications Statistical Gas Distribution Modelling for Mobile Robot Applications Örebro Studies in Technology 62 MATTEO REGGENTE Statistical Gas Distribution Modelling for Mobile Robot Applications © Matteo Reggente, 2014 Title: Statistical Gas Distribution Modelling for Mobile Robot Applications Publisher: Örebro University 2014 www.oru.se/publikationer-avhandlingar Print: Örebro University, Repro 08/14 ISSN 1650-8580 ISBN 978-91-7529-034-8 Abstract Matteo Reggente (2014): Statistical Gas Distribution Modelling for Mobile Robot Applications. Örebro Studies in Technology 62. In this dissertation, we present and evaluate algorithms for statistical gas distri- bution modelling in mobile robot applications. We derive a representation of the gas distribution in natural environments using gas measurements collected with mobile robots. The algorithms fuse different sensors readings (gas, wind and loca- tion) to create 2D or 3D maps. AbstractThroughout this thesis, the Kernel DM+V algorithm plays a central role in modelling the gas distribution. The key idea is the spatial extrapolation of the gas measurement using a Gaussian kernel. The algorithm produces four maps: the weight map shows the density of the measurements; the confidence map shows Inare thisas thesis,in which we the present model and is considered evaluate algorithms being trust forful; statistical the mean gas map distribution represents the modellingmodelled ingas mobile distribu robotstion; applications.the variance map We deriverepresent a representations the spatial structure of the ob- of the servedvariance gas of distribution the mean esti usingmate geo-referenced. gas concentration measurements collectedThe Kernel with mobile DM+V/W robots algorithm equipped incorporate with gas sensors.s wind measurements in the com- putationThroughout of the thismodels dissertation, by modifying the Kernelthe shape DM+V of the algorithmGaussian kernel plays aaccording central to role in modelling the distribution of the gases (pollutants) in natural environ- ments.the local We wind introduce direction gas and distribution magnitude mapping. algorithms that fuse different sensorsThe readingsKernel 3D (gas,-DM+V/W wind and algo location)rithm extend to creates the previous two or threealgorithm dimensional to the third mapsdimension from sparseusing a pointtri-variate samples. Gaus Thesian spatialkernel. extrapolation of the gas sensor measurementGround-truth is the evaluation key idea is of a the critical Kernel issue DM+V for gas algorithm. distribution The modelling gas sensor with measurementsmobile platforms. provide We informationpropose two aboutmethods a smallto evaluate area around gas distribution their surface, models. which interacts with the environment, and the Kernel DM+V algorithm ex- Firstly, we create a ground-truth gas distribution using a simulation environment, trapolates the measurements using a Gaussian weighting function for locations atand a certain we com distancepare the from model thes sensor with surface.this ground-truth gas distribution. Secondly, consideringThe Kernel that DM+V a good algorithm model providesshould explain four two-dimensional the measurements grid and maps. accurate The ly weightpredicts map new is ones, a graphical we evaluate representation the models of theaccording density to of their measurements; ability in inferring the confidenceunseen gas map, concentrations. highlights areas in which the model is considered being trustful becauseWe evaluate the estimate the algorithms is based on carrying a large numberout experiments of readings in different (high confidence), environments. and areas in which it is not (low confidence); the map of the mean gas dis- We start with a simulated environment and we end in urban applications, in which tribution is a graphical representation of the modelled gas distribution in the monitoredwe integrated environment; gas sensors the on map robots of the designed variance for estimate urban giveshygiene. a graphical We found rep- that resentationtypically the of models the spatial that structurecomprise ofwind the information variance of theoutperform mean estimate. the models The that variancedo not include map can the provide wind data. valuable information about the gas distribution by highlighting areas of high fluctuations that often are in close vicinity to the gas source.Keywords: statistical modelling; gas distribution mapping; mobile robots; gas sensorsThe Kernel; kernel DM+V/W density estimation; algorithm Gaussian extends the kernel previous. algorithm so that it takes into consideration that the wind is the main responsible for the disper- sionMatteo of the Reggente, gas in the School environments. of Science If the and local Technology wind information is available, adjustingÖrebro theUniversity, kernel shape SE-701 (weighting 82 Örebro, function) Sweden, according reggente@gmail to the wind direction.com and intensity improves the quality of the model, because, the model takes into consideration from where the sensed gasi patches come from and where they tend to go to. Acknowledgements First of all I am indebted to my supervisor Prof. Achim Lilienthal for giving me the opportunity to join the Mobile Robotics and Olfaction Lab at AASS and work under his guidance. I gratefully acknowledge the EU FP6 project DustBot that funded my position at Örebro University, giving me the opportunity to perform basic research without asking for a market-ready product as the first priority. Special thanks go to the anonymous reviewer, for reviewing this disserta- tion. I would like to thank Dr. Thomas Lochmatter and Prof. Hiroshi Ishida for sharing experimental data for testing the proposed algorithms. I would like to thank my colleagues and friends at AASS. Thanks Sahar Asadi, Krzysztof Charusta, Marcello Cirillo, Robert Krug, Kevin LeBlanc, Karol Niechwiadowicz, Sepideh Pashami, Federico Pecora, Todor Stoyanov and all the PhD student and senior researcher at AASS. Special thanks go to Marco Trincavelli, for the interesting scientific discussion during and after work hours. I have to thank Barbro Alvin and Kicki Ekberg for helping with the bureau- cracy and the organization of my trips. Thanks go also to our engineers: Per Sporrong that helped me setting up the robots and Bo-Lennart Silfverdal for setting up the electronic noses. I want to acknowledge also Jan Theunis and all the colleagues at the air quality measurement group at Flemish Institute for Technological Research (VITO), for giving me the opportunity to extend my background to urban air quality monitoring. I want to thank my family, Ennio, Graziella, Cristiana and Melania for their love and support. Finally, thank you Rita, for coming along with me, for your understanding, patience and always believing in me. iii Contents 1 Introduction 1 1.1 Problem Statement ......................... 3 1.2 Outline ............................... 4 1.3 Contributions ............................ 5 1.4 Publications ............................. 6 2 Background 9 2.1 Gas Dispersion in Natural Environments ............. 9 2.2 Biological Olfaction ........................ 11 2.3 Artificial Olfaction – Electronic Nose ............... 13 2.3.1 Gas Sensor Array ...................... 13 2.3.2 Data Processing Unit .................... 17 2.3.3 Delivery and Sampling Systems .............. 18 2.4 Air Pollution Monitoring ...................... 19 2.4.1 Air Pollution Monitoring using Electronic Noses ..... 20 2.4.2 Air Pollution Monitoring using Deterministic Dispersion Modelling .......................... 22 2.4.3 Air Pollution Monitoring using Statistical Modelling . 25 2.5 Mobile Robots with Electronic Noses ............... 28 2.5.1 Gas Distribution Modelling – GDM ............ 29 2.5.2 Gas Discrimination with mobile robots .......... 36 2.5.3 Gas Source Localization .................. 37 2.5.4 Trail Guidance ....................... 37 3 Experimental Setup 39 3.1 Simulation Setup .......................... 39 3.1.1 Wind Tunnel Experimental Arena ............. 40 3.1.2 Advection Model ...................... 40 3.1.3 Gas Dispersion Model ................... 42 3.1.4 Robot/Sensor Trajectory Model .............. 43 3.1.5 Gas Sensor Model ..................... 45 v vi CONTENTS 3.1.6 Simulation Output ..................... 46 3.2 Real World Experimental Setup .................. 47 3.2.1 Wind Tunnel at EPFL ................... 47 3.2.2 Enclosed Small Room with Weak Wind Filed ....... 49 3.2.3 Experiments at Örebro University ............. 51 3.3 Air Quality Monitoring in the DustBot System .......... 54 3.3.1 Ambient Monitoring Module (AMM) ........... 55 3.3.2 Gas Monitoring with the DustBot System ......... 57 4 The Kernel DM+V/W Algorithm 61 4.1 The Kernel DM+V Algorithm ................... 62 4.1.1 Parameter Selection ..................... 68 4.2 Incorporating Local Wind Information: The Kernel DM+V/W Algorithm .............................. 69 4.2.1 Local Wind Integration ................... 70 4.2.2 Parameter Selection ..................... 73 4.2.3 Kernel Position ....................... 74 4.3 Quantitative Evaluation ...................... 77 4.4 Results ................................ 77 4.4.1 Qualitative Results ..................... 79 4.4.2 Quantitative Results .................... 83 4.4.3 Discussion .......................... 86 4.5 Summary and Conclusions ..................... 91 5 Model Evaluation in Simulated Environments 93 5.1 Simulation Results in the Case of Laminar Flow ......... 94 5.1.1 Quantitative Results .................... 94 5.1.2 Mean Estimate Maps
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