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Abstract

This paper proposes an approach to robust state estimation for mobile robots with intermittent dynamics. The approach consists of identifying the robot?s mode of operation by classifying the output of onboard sensors into mode-specific contexts. The underlying technique seeks to efficiently use available sensor information to enable accurate, high-bandwidth mode identification. Context classification is combined with multiple-model filtering in order to significantly improve the accuracy of state estimates for hybrid systems. This approach is validated in simulation and shown experimentally to produce accurate estimates on a jogging robot using low-cost sensors.

Keywords

State Estimation, Classification, Multiple-Model Filtering, Hybrid Systems

Notes

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

Associated Project(s): RHex Robot

Number of pages: 6

Text Reference

Sarjoun Skaff, Alfred Rizzi, Howie Choset, and Pei-Chun Lin, "A Context-Based State Estimation Technique for Hybrid Systems," Proceedings of the 2005 IEEE International Conference on Robotics and Automation, April, 2005, pp. 3935-3940.

BibTeX Reference

@inproceedings{Skaff_2005_5026,
   author = "Sarjoun Skaff and Alfred Rizzi and Howie Choset and Pei-Chun Lin",
   title = "A Context-Based State Estimation Technique for Hybrid Systems",
   booktitle = "Proceedings of the 2005 IEEE International Conference on Robotics and Automation",
   pages = "3935-3940",
   publisher = "IEEE",
   month = "April",
   year = "2005",
}

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