Early Wildfire Detection using UAVs Integrated with Air Quality and LiDAR Sensors

Every year, wildfires burn out countless hectares of lands, resulting in ecological, environmental, and economic damage. This paper presents an energy management system that consists of an unmanned aerial vehicle (UAV) equipped with air quality and light detection and ranging (LiDAR) sensors for monitoring forests and recognizing flames early. We develop a novel approach for autonomous patrolling system. This approach has the advantage of effectively detecting wildfire incidents, while optimizing the energy consumption of the UAV’s battery to cover large areas. When a wildfire is detected, the UAV is able to transmit real-time data, such as sensor readings and LiDAR data, to the nearby communication tower. We formulate an optimization problem that minimizes the overall UAV’s energy consumption due to patrolling. Based on the pollutant dispersion mode, we propose a novel UAV patrolling solution based on genetic algorithm with the goal of maximizing the patrolling coverage of the UAV taking into account the UAV’s battery constraints. More specifically, we optimize the UAV’s flight path using a plume dispersion model to find the concentration of common gases of wildfire. Finally, simulations are presented to show the efficiency and validity of the solution.