Autonomous UAV Path Planning for Wildfire Data Collection

Teams of unmanned aerial vehicles (UAVs) can be employed in the real-time collection of data for dynamic data-driven simulations of wildfires to provide more accurate simulation predictions. The goal of this thesis was to identify and simulate path planning algorithms and inter-UAV negotiation protocols that best govern and divide responsibility among a team of autonomous UAVs continuously observing the perimeter of a wildfire containing sections of varying importance – ranging from low-value to high-value target areas. Several benchmark algorithms were implemented as well to measure the effectiveness of the new algorithms. The performance of each algorithm was measured by simulating a team with varying number of UAVs moving around the perimeter of multiple wildfires under differing conditions. The newly developed path planning and negotiation algorithms were found to increase the effectiveness of the team over the case of the benchmark algorithms. The increase in efficiency can be attributed to the UAVs’ more selective choice of destination and the equal division of responsibility between UAVs