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Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Ritter, Tobias and Euler, Juliane and Ulbrich, Stefan and Stryk, Oskar von (2014):
Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors.
In: Proceedings of the 19th IFAC World Congress, [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 and Euler, Juliane and Ulbrich, Stefan and Stryk, Oskar von
Title: Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors
Language: English
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.

Title of Book: Proceedings of the 19th IFAC World Congress
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
Identification Number: 2014:Ritter-etal
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