Dynamic Data Driven Application System for Plume Estimation Using UAVs

In this article, a full dynamic data-driven application system (DDDAS) is proposed for dynamically estimating a concentration plume and planning optimal paths for unmanned aerial vehicles (UAVs) equipped with environmental sensors. The proposed DDDAS dynamically incorporates measured data from UAVs into an environmental simulation while simultaneously steering measurement processes. In order to assimilate incomplete and noisy state observations into this system in real-time, the proper orthogonal decomposition (POD) is used to estimate the plume concentration by matching partial observations with pre-computed dominant modes in a least-square sense. In order to maximize the information gain, UAVs are dynamically driven to hot spots chosen based on the POD modes. Smoothed particle hydrodynamics (SPH) techniques are used for UAV guidance with collision and obstacle avoidance. We demonstrate the efficacy of the data assimilation and control strategies in numerical simulations. Especially, a single UAV outperforms the ten static sensors in this scenario in terms of the mean square error over the full time interval. Additionally, the multi-vehicle data collection scenarios outperform the single vehicle scenarios for both static sensors at optimal positions and UAVs controlled by SPH.

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