SBIR Phase I: Intelligent Drone Ignitions To Manage Fires

Drone Amplified, Inc., Lincoln NE

Investigators

Abstract

The broader impact/commercial potential of this project is the development of drone-based technology to manage fires more safely, efficiently, and effectively. Wildfires cause hundreds of billions of dollars in annual damages in the United States. As the size and number of wildfires increases, prescribed fires have emerged as one of the most cost-effective tools to reduce catastrophic damage by removing excess fuel for wildfires and controlling their evolution. Yet, to be most effective, prescribed fires require putting people in the proximity of fire and are often limited by the unavailability (due to cost or demand) of fast delivery mechanisms such as those provided by helicopters. This effort will result in technology that directly addresses both pain points by improving the intelligence of drone-based fire monitoring and ignition systems. This project has the potential to shape how fire management is conducted in the future, making it safer for fire personnel and enabling cost-effective prescribed fire practices that can help society to control catastrophic wildfires and better manage landscapes. This Small Business Innovation Research (SBIR) Phase I project will enable the development of drone-based technology that will transform fire management. Current drone-based ignition and monitoring systems have the potential to change the field but are hampered by their extensive reliance on human controllers. This lack of automation limits these systems? potential applicability, scale, effectiveness, and unnecessarily increases their risk. The proposed work aims to make monitoring and ignition more intelligent and scalable through: 1) specialized machine learning pipelines to characterize fire fronts and identify ground assets; 2) light-weight fire simulation engine that can provide short-horizon prediction of the fire front evolution while executing on limited computer power; and 3) distributed planning algorithms that adjust the drone's trajectory and sphere drop strategy to manipulate fire intensity while keeping ground assets, vehicles, and personnel safe. These challenges are especially difficult to address given the harsh fire environment, the weight and power constraints of commercial drones preferred for these activities, and the integration of two distinct domains, drone navigation and fire management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.