TU Darmstadt / ULB / TUbiblio

Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes

Euler, Juliane and Ritter, Tobias and Ulbrich, Stefan and Stryk, Oskar von
Ravela, Sai and Sandu, Adrian (eds.) :

Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes.
In: Dynamic Data-Driven Environmental Systems Science. Lecture Notes in Computer Science, 8964. Springer International Publishing
[Book Section] , (2015)

Abstract

Optimal coordination of multiple sensors is crucial for efficient atmospheric dispersion estimation. The proposed approach adaptively provides optimized trajectories with respect to sensor cooperation and uncertainty reduction of the process estimate. To avoid the time-consuming solution of a complex optimal control problem, estimation and vehicle control are considered separate problems linked in a sequential procedure. Based on a partial differential equation model, the Ensemble Transform Kalman Filter is applied for data assimilation and generation of observation targets offering maximum information gain. A centralized model-predictive vehicle controller simultaneously provides optimal target allocation and collision-free path planning. Extending previous work, continuous measuring is assumed, which attaches more significance to the course of the trajectories. Local attraction points are introduced to draw the sensors to regions of high uncertainty. Moreover, improved target updates increase the sampling efficiency. A simulated test case illustrates the approach in comparison to non-attracted trajectories.

Item Type: Book Section
Erschienen: 2015
Editors: Ravela, Sai and Sandu, Adrian
Creators: Euler, Juliane and Ritter, Tobias and Ulbrich, Stefan and Stryk, Oskar von
Title: Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes
Language: English
Abstract:

Optimal coordination of multiple sensors is crucial for efficient atmospheric dispersion estimation. The proposed approach adaptively provides optimized trajectories with respect to sensor cooperation and uncertainty reduction of the process estimate. To avoid the time-consuming solution of a complex optimal control problem, estimation and vehicle control are considered separate problems linked in a sequential procedure. Based on a partial differential equation model, the Ensemble Transform Kalman Filter is applied for data assimilation and generation of observation targets offering maximum information gain. A centralized model-predictive vehicle controller simultaneously provides optimal target allocation and collision-free path planning. Extending previous work, continuous measuring is assumed, which attaches more significance to the course of the trajectories. Local attraction points are introduced to draw the sensors to regions of high uncertainty. Moreover, improved target updates increase the sampling efficiency. A simulated test case illustrates the approach in comparison to non-attracted trajectories.

Title of Book: Dynamic Data-Driven Environmental Systems Science
Series Name: Lecture Notes in Computer Science
Volume: 8964
Publisher: Springer International Publishing
Divisions: Department of Computer Science
Department of Computer Science > Simulation, Systems Optimization and Robotics Group
Exzellenzinitiative
Exzellenzinitiative > Graduate Schools
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Date Deposited: 20 Jun 2016 23:26
DOI: 10.1007/978-3-319-25138-7
Identification Number: 2015:DyDESS-Euler
Related URLs:
Export:

Optionen (nur für Redakteure)

View Item View Item