Mobile Robot Wind Mapping

Abstract: 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.