3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm

This study presents an advanced multi-view drone swarm imaging system for the three-dimensional characterization of smoke plume dispersion dynamics. The system comprises a manager drone and four worker drones, each equipped with high-resolution cameras and precise GPS modules. The manager drone uses image feedback to autonomously detect and position itself above the plume, then commands the worker drones to orbit the area in a synchronized circular flight pattern, capturing multi-angle images. The camera poses of these images are first estimated, then the images are grouped in batches and processed using Neural Radiance Fields (NeRF) to generate high-resolution 3D reconstructions of plume dynamics over time. Field tests demonstrated the ability of the system to capture critical plume characteristics including volume dynamics, wind-driven directional shifts, and lofting behavior at a temporal resolution of about 1 s. The 3D reconstructions generated by this system provide unique field data for enhancing the predictive models of smoke plume dispersion and fire spread. Broadly, the drone swarm system offers a versatile platform for high resolution measurements of pollutant emissions and transport in wildfires, volcanic eruptions, prescribed burns, and industrial processes, ultimately supporting more effective fire control decisions and mitigating wildfire risks.

Autonomous Drone for Dynamic Smoke Plume Tracking

This paper presents a novel autonomous drone-based smoke plume tracking system capable of navigating and tracking plumes in highly unsteady atmospheric conditions. The system integrates advanced hardware and software and a comprehensive simulation environment to ensure robust performance in controlled and real-world settings. The quadrotor, equipped with a high-resolution imaging system and an advanced onboard computing unit, performs precise maneuvers while accurately detecting and tracking dynamic smoke plumes under fluctuating conditions. Our software implements a two-phase flight operation, i.e., descending into the smoke plume upon detection and continuously monitoring the smoke movement during in-plume tracking. Leveraging Proportional Integral-Derivative (PID) control and a Proximal Policy Optimization based Deep Reinforcement Learning (DRL) controller enables adaptation to plume dynamics. Unreal Engine simulation evaluates performance under various smoke-wind scenarios, from steady flow to complex, unsteady fluctuations, showing that while the PID controller performs adequately in simpler scenarios, the DRL-based controller excels in more challenging environments. Field tests corroborate these findings. This system opens new possibilities for drone-based monitoring in areas like wildfire management and air quality assessment. The successful integration of DRL for real-time decision-making advances autonomous drone control for dynamic environments.