Euler, Juliane ; Ritter, Tobias ; Ulbrich, Stefan ; Stryk, Oskar von
Hrsg.: Ravela, Sai ; Sandu, Adrian (2015)
Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes.
In: Dynamic Data-Driven Environmental Systems Science
doi: 10.1007/978-3-319-25138-7
Buchkapitel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Buchkapitel |
|---|---|
| Erschienen: | 2015 |
| Herausgeber: | Ravela, Sai ; Sandu, Adrian |
| Autor(en): | Euler, Juliane ; Ritter, Tobias ; Ulbrich, Stefan ; Stryk, Oskar von |
| Art des Eintrags: | Bibliographie |
| Titel: | Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes |
| Sprache: | Englisch |
| Publikationsjahr: | 2015 |
| Verlag: | Springer International Publishing |
| Buchtitel: | Dynamic Data-Driven Environmental Systems Science |
| Reihe: | Lecture Notes in Computer Science |
| Band einer Reihe: | 8964 |
| DOI: | 10.1007/978-3-319-25138-7 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 20 Jun 2016 23:26 |
| Letzte Änderung: | 19 Mär 2019 14:23 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum