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Abstract

This paper presents an approach to accurate and scalable multiple-model state estimation for hybrid systems with intermittent, multi-modal dynamics. The approach consists of using discrete-state estimation to identify a system? behavioral context and determine which motion models appropriately represent current dynamics, and which multiple-model filters are appropriate for state estimation. This improves the accuracy and scalability of conventional multiple-model state estimation. This approach is validated experimentally on a mobile robot that exhibits multi-modal dynamics.

Keywords

Hidden Markov Models, Timed Automata, Multiple-Model Filtering

Notes

Associated Lab(s) / Group(s): Microdynamic Systems Laboratory

Associated Project(s): RHex Robot

Number of pages: 6

Text Reference

Sarjoun Skaff, Howie Choset, and Alfred Rizzi, "Context Identification for Efficient Multiple-Model State Estimation," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November, 2007, pp. 2435-2440.

BibTeX Reference

@inproceedings{Skaff_2007_6001,
   author = "Sarjoun Skaff and Howie Choset and Alfred Rizzi",
   title = "Context Identification for Efficient Multiple-Model State Estimation",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
   pages = "2435-2440",
   month = "November",
   year = "2007",
   number = "2",
}

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