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Proceedings Paper

Dynamic sensor action selection with Bayesian decision analysis
Author(s): Steen Kristensen; Volker Hansen; Konstantin Kondak
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Paper Abstract

The aim of this work is to create a framework for the dynamic planning of sensor actions for an autonomous mobile robot. The framework uses Bayesian decision analysis, i.e., a decision-theoretic method, to evaluate possible sensor actions and selecting the most appropriate ones given the available sensors and what is currently known about the state of the world. Since sensing changes the knowledge of the system and since the current state of the robot (task, position, etc.) determines what knowledge is relevant, the evaluation and selection of sensing actions is an on-going process that effectively determines the behavior of the robot. The framework has been implemented on a real mobile robot and has been proven to be able to control in real-time the sensor actions of the system. In current work we are investigating methods to reduce or automatically generate the necessary model information needed by the decision- theoretic method to select the appropriate sensor actions.

Paper Details

Date Published: 9 October 1998
PDF: 10 pages
Proc. SPIE 3523, Sensor Fusion and Decentralized Control in Robotic Systems, (9 October 1998); doi: 10.1117/12.327000
Show Author Affiliations
Steen Kristensen, Daimler-Benz AG (Germany)
Volker Hansen, Daimler-Benz AG (Germany)
Konstantin Kondak, Daimler-Benz AG (Germany)


Published in SPIE Proceedings Vol. 3523:
Sensor Fusion and Decentralized Control in Robotic Systems
Paul S. Schenker; Gerard T. McKee, Editor(s)

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