A decentralized control policy for adaptive information gathering in hazardous environments
This paper proposes an algorithm for driving a group of resource-constrained robots with noisy sensors to localize an unknown number of targets in an environment, while avoiding hazards at unknown positions that cause the robots to fail. The algorithm is based upon the analytic gradient of mutual in...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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2014
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| Online Access: | http://hdl.handle.net/1721.1/90616 https://orcid.org/0000-0001-5473-3566 |
| _version_ | 1826212364748324864 |
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| author | Dames, Philip Schwager, Mac Kumar, Vijay Rus, Daniela L. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Dames, Philip Schwager, Mac Kumar, Vijay Rus, Daniela L. |
| author_sort | Dames, Philip |
| collection | MIT |
| description | This paper proposes an algorithm for driving a group of resource-constrained robots with noisy sensors to localize an unknown number of targets in an environment, while avoiding hazards at unknown positions that cause the robots to fail. The algorithm is based upon the analytic gradient of mutual information of the target locations and measurements and offers two primary improvements over previous algorithms [6], [13]. Firstly, it is decentralized. This follows from an approximation to mutual information based upon the fact that the robots' sensors and environmental hazards have a finite area of influence. Secondly, it allows targets to be localized arbitrarily precisely with limited computational resources. This is done using an adaptive cellular decomposition of the environment, so that only areas that likely contain a target are given finer resolution. The estimation is built upon finite set statistics, which provides a rigorous, probabilistic framework for multi-target tracking. The algorithm is shown to perform favorably compared to existing approximation methods in simulation. |
| first_indexed | 2024-09-23T15:20:14Z |
| format | Article |
| id | mit-1721.1/90616 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:20:14Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | mit-1721.1/906162022-10-02T02:16:44Z A decentralized control policy for adaptive information gathering in hazardous environments Dames, Philip Schwager, Mac Kumar, Vijay Rus, Daniela L. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. School of Engineering Rus, Daniela L. This paper proposes an algorithm for driving a group of resource-constrained robots with noisy sensors to localize an unknown number of targets in an environment, while avoiding hazards at unknown positions that cause the robots to fail. The algorithm is based upon the analytic gradient of mutual information of the target locations and measurements and offers two primary improvements over previous algorithms [6], [13]. Firstly, it is decentralized. This follows from an approximation to mutual information based upon the fact that the robots' sensors and environmental hazards have a finite area of influence. Secondly, it allows targets to be localized arbitrarily precisely with limited computational resources. This is done using an adaptive cellular decomposition of the environment, so that only areas that likely contain a target are given finer resolution. The estimation is built upon finite set statistics, which provides a rigorous, probabilistic framework for multi-target tracking. The algorithm is shown to perform favorably compared to existing approximation methods in simulation. United States. Air Force Office of Scientific Research (Grant FA9550-10-1-0567) United States. Office of Naval Research (Grant N00014-07-1-0829) United States. Office of Naval Research (Grant N00014-09-1-1051) United States. Office of Naval Research (Grant N00014-09-1-1031) 2014-10-07T19:50:21Z 2014-10-07T19:50:21Z 2012-12 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-2066-5 978-1-4673-2065-8 978-1-4673-2063-4 978-1-4673-2064-1 0743-1546 http://hdl.handle.net/1721.1/90616 Dames, Philip, Mac Schwager, Vijay Kumar, and Daniela Rus. “A Decentralized Control Policy for Adaptive Information Gathering in Hazardous Environments.” 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) (December 2012). https://orcid.org/0000-0001-5473-3566 en_US http://dx.doi.org/10.1109/CDC.2012.6426239 Proceedings of the 2012 51st IEEE Conference on Decision and Control (CDC) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Other univ. web domain |
| spellingShingle | Dames, Philip Schwager, Mac Kumar, Vijay Rus, Daniela L. A decentralized control policy for adaptive information gathering in hazardous environments |
| title | A decentralized control policy for adaptive information gathering in hazardous environments |
| title_full | A decentralized control policy for adaptive information gathering in hazardous environments |
| title_fullStr | A decentralized control policy for adaptive information gathering in hazardous environments |
| title_full_unstemmed | A decentralized control policy for adaptive information gathering in hazardous environments |
| title_short | A decentralized control policy for adaptive information gathering in hazardous environments |
| title_sort | decentralized control policy for adaptive information gathering in hazardous environments |
| url | http://hdl.handle.net/1721.1/90616 https://orcid.org/0000-0001-5473-3566 |
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