Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von (2014)
Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles’ motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von |
| Art des Eintrags: | Bibliographie |
| Titel: | Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Buchtitel: | Proceedings of the 19th IFAC World Congress |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles’ motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained. |
| 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: | 08 Apr 2019 13:16 |
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