2026-09-18T21:45:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/458872022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicholas Roy.
Park, Sooho, S.M. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2009-06-30T16:32:43Z
2009-06-30T16:32:43Z
2008
2008
http://hdl.handle.net/1721.1/45887
320436167
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 104-108).
Through the combined use of regression techniques, we will learn models of the uncertainty propagation efficiently and accurately to replace computationally intensive Monte- Carlo simulations in informative path planning. This will enable us to decrease the uncertainty of the weather estimates more than current methods by enabling the evaluation of many more candidate paths given the same amount of resources. The learning method and the path planning method will be validated by the numerical experiments using the Lorenz-2003 model [32], an idealized weather model.
by Sooho Park.
S.M.
108 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Learning for informative path planning
Thesis
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