Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von (2014)
Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors.
Conference or Workshop Item
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2014 |
| Creators: | Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von |
| Type of entry: | Bibliographie |
| Title: | Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors |
| Language: | English |
| Date: | 2014 |
| Book Title: | Proceedings of the 19th IFAC World Congress |
| Corresponding Links: | |
| 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. |
| Identification Number: | 2014:Ritter-etal |
| Divisions: | 20 Department of Computer Science 20 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 |
| Last Modified: | 08 Apr 2019 13:16 |
| PPN: | |
| Corresponding Links: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Send an inquiry |
Options (only for editors)
 |
Show editorial Details |
Drucken
Impressum