Multi-vehicle Control and Estimation for Cooperative Atmospheric Sampling

This paper presents control and estimation approaches for multiple vehicles to cooperatively sample atmospheric variables, with a focus on wind estimation, in complex environments. The technology is encapsulated in the WINDSENSE system. The collected data could be used to initialize weather models for nowcasting or forecasting, assess the fidelity of a meteorological model, assist in understanding plume dynamics or tracking plumes, provide real-time data for fire controls or wildfire fighting, or locate the source of a chemical, biological, radiological, or nuclear (CBRN) source. The wind estimation approach utilizes a Bayesian formulation with a process model found using system identification techniques. The model structure of the identified dynamics builds on prior work by the authors and combines first-principles and experimental data collection to generate a model that is valid over a wide range of the flight envelope. This enables the wind estimator to also be viable over the modeled domain, allowing for wind estimation at trim and quasi-trim conditions and during dynamic maneuvers. Performance of the wind estimator is validated in field testing via comparison with a research-grade ultrasonic anemometer. A decentralized controller enables coordinated flight amongst multiple vehicles to maintain a desired formation geometry and reshape the formation to optimize data collection for different environments or mission objectives. Sampling strategies for a swarm of vehicles are developed through analysis of large-eddy simulation studies of wind and particle dispersion using the PALM facility.