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Carnegie Mellon Robotics Institute

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Abstract

We describe a heuristic search algorithm for generating optimal plans in a new class of decision problem, characterised by the incorporation of hidden state. The approach exploits the nature of the hidden state to reduce the state space by orders of magnitude. It then interleaves AO*-type heuristic expansion of the reduced space with forwards and backwards propagation phases to produce a solution in a fraction of the time required by other techniques. Results are provided on an outdoor path planning application.

Keywords

Path Planning

Notes

Associated Center(s) / Consortia: Field Robotics Center

Number of pages: 8

Text Reference

David Ferguson, Anthony (Tony) Stentz, and Sebastian Thrun, "PAO* for Planning with Hidden State," Proceedings of the 2004 IEEE International Conference on Robotics and Automation, April, 2004, pp. 2840 - 2847.

BibTeX Reference

@inproceedings{Ferguson_2004_4591,
   author = "David Ferguson and Anthony (Tony) Stentz and Sebastian Thrun",
   title = "PAO* for Planning with Hidden State",
   booktitle = "Proceedings of the 2004 IEEE International Conference on Robotics and Automation",
   pages = "2840 - 2847",
   month = "April",
   year = "2004",
   volume = "3",
}

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