Combining local and global optimization for planning and control in information space
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 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/1721.1/45281 |
| _version_ | 1826214653648175104 |
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| author | Huynh, Vu Anh |
| author2 | Nicholas Roy. |
| author_facet | Nicholas Roy. Huynh, Vu Anh |
| author_sort | Huynh, Vu Anh |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. |
| first_indexed | 2024-09-23T16:08:54Z |
| format | Thesis |
| id | mit-1721.1/45281 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:08:54Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/452812019-04-11T08:22:28Z Combining local and global optimization for planning and control in information space Huynh, Vu Anh Nicholas Roy. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Computation for Design and Optimization Program. Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. Includes bibliographical references (leaves 99-102). This thesis presents a novel algorithm, called the parametric optimized belief roadmap (POBRM), to address the problem of planning a trajectory for controlling a robot with imperfect state information under uncertainty. This question is formulated abstractly as a partially observable stochastic shortest path (POSSP) problem. We assume that the feature-based map of a region is available to assist the robot's decision-making. The POBRM is a two-phase algorithm that combines local and global optimization. In an offline phase, we construct a belief graph by probabilistically sampling points around the features that potentially provide the robot with valuable information. Each edge of the belief graph stores two transfer functions to predict the cost and the conditional covariance matrix of a final state estimate if the robot follows this edge given an initial mean and covariance. In an online phase, a sub-optimal trajectory is found by the global Dijkstra's search algorithm, which ensures the balance between exploration and exploitation. Moreover, we use the iterative linear quadratic Gaussian algorithm (iLQG) to find a locally-feedback control policy in continuous state and control spaces to traverse the sub-optimal trajectory. We show that, under some suitable technical assumptions, the error bound of a sub-optimal cost compared to the globally optimal cost can be obtained. The POBRM algorithm is not only robust to imperfect state information but also scalable to find a trajectory quickly in high-dimensional systems and environments. In addition, the POBRM algorithm is capable of answering multiple queries efficiently. We also demonstrate performance results by 2D simulation of a planar car and 3D simulation of an autonomous helicopter. by Vu Anh Huynh. S.M. 2009-04-29T17:20:02Z 2009-04-29T17:20:02Z 2008 2008 Thesis http://hdl.handle.net/1721.1/45281 311815419 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 102 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Computation for Design and Optimization Program. Huynh, Vu Anh Combining local and global optimization for planning and control in information space |
| title | Combining local and global optimization for planning and control in information space |
| title_full | Combining local and global optimization for planning and control in information space |
| title_fullStr | Combining local and global optimization for planning and control in information space |
| title_full_unstemmed | Combining local and global optimization for planning and control in information space |
| title_short | Combining local and global optimization for planning and control in information space |
| title_sort | combining local and global optimization for planning and control in information space |
| topic | Computation for Design and Optimization Program. |
| url | http://hdl.handle.net/1721.1/45281 |
| work_keys_str_mv | AT huynhvuanh combininglocalandglobaloptimizationforplanningandcontrolininformationspace |