Dynamic Data Driven Application System for Plume Estimation Using UAVs

Authors:
Liqian Peng;Doug Lipinski;Kamran Mohseni
Affiliations:
Department of Mechanical and Aerospace Engineering, Institute for Networked Autonomous Systems, University of Florida, Gainesville, USA 32611;Department of Mechanical and Aerospace Engineering, Institute for Networked Autonomous Systems, University of Florida, Gainesville, USA 32611;Department of Mechanical and Aerospace Engineering, Institute for Networked Autonomous Systems, University of Florida, Gainesville, USA 32611 and Department of Electrical and Computer Engineering, ...
Venue:
Journal of Intelligent and Robotic Systems
Year:
2014

Citing 13
Cited 0

Motion planning with uncertainty: a landmark approach

Artificial Intelligence - Special volume on planning and scheduling
Stable fluids

Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Dynamic Iteration Using Reduced Order Models: A Method for Simulation of Large Scale Modular Systems

SIAM Journal on Numerical Analysis
DYNAMIC DATA-DRIVEN INVERSION FOR TERASCALE SIMULATIONS: REAL-TIME IDENTIFICATION OF AIRBORNE CONTAMINANTS

SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
SensorFlock: an airborne wireless sensor network of micro-air vehicles

Proceedings of the 5th international conference on Embedded networked sensor systems
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies

The Journal of Machine Learning Research
Compressed Sensing and Time-Parallel Reduced-Order Modeling for Structural Health Monitoring Using a DDDAS

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
A wildland fire model with data assimilation

Mathematics and Computers in Simulation
Injecting Dynamic Real-Time Data into a DDDAS for Forest Fire Behavior Prediction

ICCS 2009 Proceedings of the 9th International Conference on Computational Science
An Information Roadmap Method for Robotic Sensor Path Planning

Journal of Intelligent and Robotic Systems
Nonlinear Model Reduction via Discrete Empirical Interpolation

SIAM Journal on Scientific Computing
Dealing with midair collisions in dense collective aerial systems

Journal of Field Robotics
Inversion of airborne contaminants in a regional model

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.