Robust planning for unmanned underwater vehicles
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2014
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| Online Access: | http://hdl.handle.net/1721.1/84854 |
| _version_ | 1826193934078967808 |
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| author | Frost, Emily Anne |
| author2 | Dimitris Bertsimas and Julie Shah. |
| author_facet | Dimitris Bertsimas and Julie Shah. Frost, Emily Anne |
| author_sort | Frost, Emily Anne |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. |
| first_indexed | 2024-09-23T09:47:35Z |
| format | Thesis |
| id | mit-1721.1/84854 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:47:35Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/848542019-04-11T11:34:50Z Robust planning for unmanned underwater vehicles Robust planning for UUVs Frost, Emily Anne Dimitris Bertsimas and Julie Shah. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 59-60). In this thesis, I design and implement a novel method of schedule and path selection between predetermined waypoints for unmanned underwater vehicles under uncertainty. The problem is first formulated as a mixed-integer optimization model and subsequently uncertainty is addressed using a robust optimization approach. Solutions were tested through simulation and computational results are presented which indicate that the robust approach handles larger problems than could previously be solved in a reasonable running time while preserving a high level of robustness. This thesis demonstrates that the robust methods presented can solve realistic-sized problems in reasonable runtimes - a median of ten minutes and a mean of thirty minutes for 32 tasks - and that the methods perform well both in terms of expected reward and robustness to disturbances in the environment. The latter two results are obtained by simulating solutions given by the deterministic method, a naive robust method, and finally the two restricted affine robust policies. The two restricted affine policies consistently show an expected reward of nearly 100%, while the deterministic and naive robust methods achieve approximately 50% of maximum reward possible. by Emily Anne Frost. S.M. 2014-02-10T16:54:51Z 2014-02-10T16:54:51Z 2013 Thesis http://hdl.handle.net/1721.1/84854 868235613 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 60 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Operations Research Center. Frost, Emily Anne Robust planning for unmanned underwater vehicles |
| title | Robust planning for unmanned underwater vehicles |
| title_full | Robust planning for unmanned underwater vehicles |
| title_fullStr | Robust planning for unmanned underwater vehicles |
| title_full_unstemmed | Robust planning for unmanned underwater vehicles |
| title_short | Robust planning for unmanned underwater vehicles |
| title_sort | robust planning for unmanned underwater vehicles |
| topic | Operations Research Center. |
| url | http://hdl.handle.net/1721.1/84854 |
| work_keys_str_mv | AT frostemilyanne robustplanningforunmannedunderwatervehicles AT frostemilyanne robustplanningforuuvs |