A gradient optimization approach to adaptive multi-robot control
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/55256 |
| _version_ | 1826216825006850048 |
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| author | Schwager, Mac |
| author2 | Daniela Rus. |
| author_facet | Daniela Rus. Schwager, Mac |
| author_sort | Schwager, Mac |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009. |
| first_indexed | 2024-09-23T16:54:02Z |
| format | Thesis |
| id | mit-1721.1/55256 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:54:02Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/552562019-04-10T08:42:44Z A gradient optimization approach to adaptive multi-robot control Schwager, Mac Daniela Rus. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 181-190). This thesis proposes a unified approach for controlling a group of robots to reach a goal configuration in a decentralized fashion. As a motivating example, robots are controlled to spread out over an environment to provide sensor coverage. This example gives rise to a cost function that is shown to be of a surprisingly general nature. By changing a single free parameter, the cost function captures a variety of different multi-robot objectives which were previously seen as unrelated. Stable, distributed controllers are generated by taking the gradient of this cost function. Two fundamental classes of multi-robot behaviors are delineated based on the convexity of the underlying cost function. Convex cost functions lead to consensus (all robots move to the same position), while any other behavior requires a nonconvex cost function. The multi-robot controllers are then augmented with a stable on-line learning mechanism to adapt to unknown features in the environment. In a sensor coverage application, this allows robots to learn where in the environment they are most needed, and to aggregate in those areas. The learning mechanism uses communication between neighboring robots to enable distributed learning over the multi-robot system in a provably convergent way. Three multi-robot controllers are then implemented on three different robot platforms. Firstly, a controller for deploying robots in an environment to provide sensor coverage is implemented on a group of 16 mobile robots. (cont.) They learn to aggregate around a light source while covering the environment. Secondly, a controller is implemented for deploying a group of three flying robots with downward facing cameras to monitor an environment on the ground. Thirdly, the multi-robot model is used as a basis for modeling the behavior of a herd of cows using a system identification approach. The controllers in this thesis are distributed, theoretically proven, and implemented on multi-robot platforms. by Mac Schwager. Ph.D. 2010-05-25T21:10:57Z 2010-05-25T21:10:57Z 2009 2009 Thesis http://hdl.handle.net/1721.1/55256 612385503 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 190 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Schwager, Mac A gradient optimization approach to adaptive multi-robot control |
| title | A gradient optimization approach to adaptive multi-robot control |
| title_full | A gradient optimization approach to adaptive multi-robot control |
| title_fullStr | A gradient optimization approach to adaptive multi-robot control |
| title_full_unstemmed | A gradient optimization approach to adaptive multi-robot control |
| title_short | A gradient optimization approach to adaptive multi-robot control |
| title_sort | gradient optimization approach to adaptive multi robot control |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/55256 |
| work_keys_str_mv | AT schwagermac agradientoptimizationapproachtoadaptivemultirobotcontrol AT schwagermac gradientoptimizationapproachtoadaptivemultirobotcontrol |