A scalable information theoretic approach to distributed robot coordination
This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IEEE
2021
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| Online Access: | https://hdl.handle.net/1721.1/137084 |
| _version_ | 1811068556591235072 |
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| author | Julian, Brian J. Angermann, Michael Schwager, Mac Rus, Daniela |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Julian, Brian J. Angermann, Michael Schwager, Mac Rus, Daniela |
| author_sort | Julian, Brian J. |
| collection | MIT |
| description | This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots. © 2011 IEEE. |
| first_indexed | 2024-09-23T07:57:45Z |
| format | Article |
| id | mit-1721.1/137084 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T07:57:45Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1370842023-01-30T21:30:12Z A scalable information theoretic approach to distributed robot coordination Julian, Brian J. Angermann, Michael Schwager, Mac Rus, Daniela Lincoln Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots. © 2011 IEEE. 2021-11-02T14:14:22Z 2021-11-02T14:14:22Z 2011-09 2019-07-16T14:39:53Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137084 Julian, Brian J., Angermann, Michael, Schwager, Mac and Rus, Daniela. 2011. "A scalable information theoretic approach to distributed robot coordination." en 10.1109/iros.2011.6095127 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE Other repository |
| spellingShingle | Julian, Brian J. Angermann, Michael Schwager, Mac Rus, Daniela A scalable information theoretic approach to distributed robot coordination |
| title | A scalable information theoretic approach to distributed robot coordination |
| title_full | A scalable information theoretic approach to distributed robot coordination |
| title_fullStr | A scalable information theoretic approach to distributed robot coordination |
| title_full_unstemmed | A scalable information theoretic approach to distributed robot coordination |
| title_short | A scalable information theoretic approach to distributed robot coordination |
| title_sort | scalable information theoretic approach to distributed robot coordination |
| url | https://hdl.handle.net/1721.1/137084 |
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