Autonomous Drone Swarming for 3D Mapping of Atmospheric Particle Transport from Micrometer to Kilometer Scale

Understanding particle dispersion from wildfires is essential for modeling its impact on regional air quality and global climate. However, field characterization across the full spectrum of particle behaviors, from microscale aerosols to kilometer scale plumes, presents significant technical challenges. To address this, we introduce an autonomous drone swarm system that includes both quadrotor drones (for maneuverability) and vertical takeoff and landing drones (for extended coverage and longer flight duration). The system features a manager drone that coordinates multiple worker drones, each equipped with computer vision cameras for navigation and plume imaging, as well as holographic microscopy sensors for detailed analysis of particle concentration, morphology, and type. The manager drone analyzes plume structures in real time and uses flow intelligent artificial intelligence to guide the worker drones, optimizing their formation and flight paths to reconstruct 3D smoke plume geometry and particle distributions. This system, developed using a simulated environment that integrates fluid dynamics and drone operation, was successfully deployed during the Cedar Creek prescribed burn experiments, demonstrating its effectiveness in real world applications. This platform offers a versatile tool not only for fundamental studies of atmospheric particle transport phenomena, such as dust, volcanic ash, and pollen dispersion, but also for practical applications in wildfire response, air quality assessment, and broader environmental monitoring.