Abstract

In recent years, there has been an increasing interest in the application of unmanned aerial vehicles in forest fire monitoring and detection systems. Armed with unmanned aerial vehicles (UAVs), firefighters on the ground can get a bird's-eye view of the terrain, respond to forest fires quickly, distribute resources, and ultimately save lives and properties. In practice, wind behaviors have significant impacts on both the performance of UAV and forest fire situations. However, current wind measurement and estimation relies on data gathered from ground weather stations that are often located several kilometers away from the forest fire regions. As a result, it is challenging to maintain the performance and assess the forest fire situations properly with the obtained wind information.

This thesis investigates the problems of the wind estimation and control of unmanned aerial vehicles with application to forest fire surveillance. To develop UAVs as remote wind sensing platforms, a two-stage particle filter-based approach is proposed to estimate winds from quadrotor motion. Based on the estimated wind information, an active wind rejection control strategy is designed to maintain the performance of a quadrotor UAV in the presence of unknown winds. Then, the active wind rejection control strategy is developed for the formation control of multiple UAVs to ensure their cooperative tracking capability. Finally, based on the wind data and fire observations collected by UAVs, a forest fire monitoring scheme is designed to accurately estimate the situation of wind-affected forest fires.