Unifying model-based programming and randomized path planning through optimal search
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/18056 |
| _version_ | 1826216128116948992 |
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| author | Walcott, Aisha, 1978- |
| author2 | Brian Williams. |
| author_facet | Brian Williams. Walcott, Aisha, 1978- |
| author_sort | Walcott, Aisha, 1978- |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. |
| first_indexed | 2024-09-23T16:42:45Z |
| format | Thesis |
| id | mit-1721.1/18056 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:42:45Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/180562019-04-12T15:56:23Z Unifying model-based programming and randomized path planning through optimal search Walcott, Aisha, 1978- Brian Williams. 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, 2004. Includes bibliographical references (p. 120-122). The deployment of robots at the World Trade Center (WTC) site after September 11, 2001, highlighted the potential for robots to aid in search and rescue missions that pose great threats and challenges to humans. However, robots that are tele-operated and tethered for power and communication are restricted in terms of their operational area. Thus, rescue robots must be equipped with onboard autonomy that enables them to select feasible plans on their own, within their physical and computational limitations. There are three main characteristics that a rescue robot's onboard system must posses. First, the system must be able to generate plans for mobile systems, that is, plans with activities and paths. Second, in order to operate as efficiently as possible, particularly in emergency situations, the system must be globally optimal. Third, the system must be able to generate plans quickly. This thesis introduces a novel autonomous control system that interleaves methods for spatial and activity planning, by merging model-based programming with roadmap-based path planning. The primary contributions are threefold. The first contribution is a model that represents possible mission strategies with activities that have cost and are constrained to a location. The second is an optimal pre-planner that reasons through the possible mission strategies in order to quickly find the optimal feasible strategy. The third contribution is a unified, global activity and path planning system. The system unifies the optimal pre-planner with a randomized roadmap-based path planner, in order to find the optimal feasible strategy to achieve a mission. The impact of these contributions is highlighted in the context of an urban search and rescue (USAR) mission. by Aisha Walcott. S.M. 2005-06-02T19:47:59Z 2005-06-02T19:47:59Z 2004 2004 Thesis http://hdl.handle.net/1721.1/18056 57396736 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 122 p. 7312414 bytes 7327793 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Walcott, Aisha, 1978- Unifying model-based programming and randomized path planning through optimal search |
| title | Unifying model-based programming and randomized path planning through optimal search |
| title_full | Unifying model-based programming and randomized path planning through optimal search |
| title_fullStr | Unifying model-based programming and randomized path planning through optimal search |
| title_full_unstemmed | Unifying model-based programming and randomized path planning through optimal search |
| title_short | Unifying model-based programming and randomized path planning through optimal search |
| title_sort | unifying model based programming and randomized path planning through optimal search |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/18056 |
| work_keys_str_mv | AT walcottaisha1978 unifyingmodelbasedprogrammingandrandomizedpathplanningthroughoptimalsearch |