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.
2014. , s. 91