Learning for informative path planning
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/45887 |
| _version_ | 1811093834299342848 |
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| author | Park, Sooho, S.M. Massachusetts Institute of Technology |
| author2 | Nicholas Roy. |
| author_facet | Nicholas Roy. Park, Sooho, S.M. Massachusetts Institute of Technology |
| author_sort | Park, Sooho, S.M. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. |
| first_indexed | 2024-09-23T15:51:26Z |
| format | Thesis |
| id | mit-1721.1/45887 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:51:26Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/458872019-04-12T16:00:39Z Learning for informative path planning Park, Sooho, S.M. Massachusetts Institute of Technology Nicholas Roy. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2009-06-30T16:32:43Z 2009-06-30T16:32:43Z 2008 2008 Thesis http://hdl.handle.net/1721.1/45887 320436167 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 108 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Park, Sooho, S.M. Massachusetts Institute of Technology Learning for informative path planning |
| title | Learning for informative path planning |
| title_full | Learning for informative path planning |
| title_fullStr | Learning for informative path planning |
| title_full_unstemmed | Learning for informative path planning |
| title_short | Learning for informative path planning |
| title_sort | learning for informative path planning |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/45887 |
| work_keys_str_mv | AT parksoohosmmassachusettsinstituteoftechnology learningforinformativepathplanning |