AAAI Publications, Twenty-Third International Conference on Automated Planning and Scheduling

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Domain Predictive Control Under Uncertain Numerical State Information
Johannes Löhr, Patrick Eyerich, Stefan Winkler, Bernhard Nebel

Last modified: 2013-06-02


In planning, hybrid system states consisting of logical and numerical variables are usually assumed to be completely known. In particular, for numerical state variables full knowledge of their exact values is assumed. However, in real world applications states are results of noisy measurements and imperfect actuators. Therefore, a planned sequence of state transitions might fail to lead a hybrid system to the desired goal. We show how to propagate and reason about uncertain state information directly in the planning process, enabling hybrid systems to find plans that satisfy numerical goals with predefined confidence.


planning; hybrid systems; domain predictive control; covariance propagation

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