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 (BDA) 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.

Citations

5665	Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference - Pearl - 1988
719	FV: An Introduction to Bayesian Networks - Jensen - 1996
298	Active perception - Bajcsy - 1988
288	Planning and Control - Dean, Wellman - 1991
182	Decision Analysis: Introductory Lectures on Choices under Uncertainty - Raiffa - 1968
166	What are Plans for - Agre, Chapman - 1990
119	A survey of sensor planning in computer vision - Tarabanis, Allen, et al. - 1995
108	A critic of pure reason - McDermott - 1987
60	Internalized plans: a representation for action resources - Payton - 1990
34	Task-Directed Sensor Fusion and Planning - A Computational Approach - Hager - 1990
13	A game theoretic approach to integration of modules - Bozma, Duncan - 1994
6	Sensor Planning with Bayesian Decision Analysis - Kristensen - 1996
4	Sensor selection in a fusion process: a fuzzy approach - GIRAUD, JOUVENCEL - 1994
3	Sensor planning with hierarchically distributed perception net - Lee, Zhao - 1994