Abstract
In this paper ongoing work on an approach for planning sensing actions and controlling intelligent, purposive robotic systems is presented. The method uses Bayesian Decision Analysis for deciding what sensing actions should be performed. This offers a probabilistic framework that provides a more dynamic and modular behaviour than traditional rule based planners. Experiments show that the Bayesian sensor planning strategy is capable of controlling an autonomous mobile robot operating in partly known environments.
This work was done at the GRASP Laboratory, University of Pennsylvania, USA.
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© 1996 Springer-Verlag Berlin Heidelberg
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Kristensen, S. (1996). Sensor planning with bayesian decision theory. In: Dorst, L., van Lambalgen, M., Voorbraak, F. (eds) Reasoning with Uncertainty in Robotics. RUR 1995. Lecture Notes in Computer Science, vol 1093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013972
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DOI: https://doi.org/10.1007/BFb0013972
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