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|a Undurti, Aditya
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|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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|a How, Jonathan P.
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|a Undurti, Aditya
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|a How, Jonathan P.
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|a How, Jonathan P.
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|a An online algorithm for constrained POMDPs
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|b Institute of Electrical and Electronics Engineers,
|c 2011-08-31T15:29:46Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/65564
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|a This work seeks to address the problem of planning in the presence of uncertainty and constraints. Such problems arise in many situations, including the basis of this work, which involves planning for a team of first responders (both humans and robots) operating in an urban environment. The problem is framed as a Partially-Observable Markov Decision Process (POMDP) with constraints, and it is shown that even in a relatively simple planning problem, modeling constraints as large penalties does not lead to good solutions. The main contribution of the work is a new online algorithm that explicitly ensures constraint feasibility while remaining computationally tractable. Its performance is demonstrated on an example problem and it is demonstrated that our online algorithm generates policies comparable to an offline constrained POMDP algorithm.
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|a United States. Office of Naval Research (grant N00014-07-1-0749)
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|a en_US
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|a Article
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|t 2010 IEEE International Conference on Robotics and Automation (ICRA)
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