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Mobile Robot Wind Mapping
Örebro University, School of Science and Technology, Örebro University, Sweden.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Statistical gas distribution mapping has recently become a prominent research area in

the robotics community. Gas distribution mapping using mobile robots aims for building

map of gas dispersion in an unknown environment using the sampled gas concentrations

accompanied by the corresponding atmospheric variables. In this context, wind is considered

as one of the main driving forces and recently exploited as an environmental

bias in the the modelling process. However, the existing approaches utilizing the wind

data are based on very simple averaging window methods which do not take the specic

spatio-temporal wind variations into account appropriately.

In the current thesis work, under the heading of statistical wind modelling, the various

aspects of the existing approaches to model both temporal and spatial wind variations

are studied. Accordingly, in the undertaking of

Mobile Robot Wind Mapping (MRWM)

task, three individual methods for statistically

wind speed modelling, wind direction

modelling

and spatial wind mapping are proposed and implemented.

Particularly, wind speed is modelled in form of a Gaussian distribution where the valid

averaging scale is dened using an online adaptive approach, namely

Time-Dependent

Memory Method (TDMM)

. The wind direction is modelled by means of the mixturemodel

of Von-Mises distribution and for the spatial mapping of modelled wind data, a

recursive approach based on Linear Kalman lter is utilized. The proposed approaches

for statistically wind speed and direction modelling are applied to and evaluated by

real wind data, collected specically for this project. The wind mapping algorithm is

implemented and tested using simulated data.

Place, publisher, year, edition, pages
2014. , 91 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-34606OAI: oai:DiVA.org:oru-34606DiVA: diva2:710697
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2014-04-08 Created: 2014-04-08 Last updated: 2014-04-08Bibliographically approved

Open Access in DiVA

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
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  • Other locale
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Output format
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