Autonomous drone swarm system for in situ characterization of atmospheric particle dispersion

Understanding aerosol dispersion from wildfires is crucial for enhancing air quality and radiative forcing models. However, due to measurement challenges, field data on aerosol dispersion, which is strongly influenced by properties such as concentration, morphology, and composition, is scarce. To bridge this gap, we've introduced an autonomous drone swarm system. This system, comprising four drones equipped with a digital holographic sensor, uses machine vision for autonomous flight guidance, enabling precise tracking and measurement of smoke plumes. A significant development has been a fully simulated environment that integrates fluid dynamics, drone flight and control, and machine vision. This environment has been instrumental in refining drone control systems and testing swarm control strategies, particularly under simulated smoke flow conditions. The system has been successfully deployed in Cedar Creek prescribed burn experiments, providing valuable data on aerosol properties and dispersion patterns. These advancements revolutionize in situ characterization of wildfire smoke aerosols, providing real-time data for air quality and climate science, while also offering a versatile tool for studying other atmospheric particle transport phenomena like dust and pollen dispersion.