Decentralized cooperative trajectory estimation for autonomous underwater vehicles
Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/97580 https://orcid.org/0000-0002-8863-6550 https://orcid.org/0000-0003-2492-6660 |
| _version_ | 1826200281114738688 |
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| author | Paull, Liam Seto, Mae Leonard, John Joseph |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Paull, Liam Seto, Mae Leonard, John Joseph |
| author_sort | Paull, Liam |
| collection | MIT |
| description | Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach. |
| first_indexed | 2024-09-23T11:34:04Z |
| format | Article |
| id | mit-1721.1/97580 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:34:04Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/975802022-10-01T04:27:03Z Decentralized cooperative trajectory estimation for autonomous underwater vehicles Paull, Liam Seto, Mae Leonard, John Joseph Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mechanical Engineering Paull, Liam Leonard, John Joseph Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach. Natural Sciences and Engineering Research Council of Canada Defense Research and Development Canada United States. Office of Naval Research (Grant N00014-13-1-0588) 2015-06-30T15:15:14Z 2015-06-30T15:15:14Z 2014-09 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-6934-0 978-1-4799-6931-9 http://hdl.handle.net/1721.1/97580 Paull, Liam, Mae Seto, and John J. Leonard. “Decentralized Cooperative Trajectory Estimation for Autonomous Underwater Vehicles.” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (September 2014). https://orcid.org/0000-0002-8863-6550 https://orcid.org/0000-0003-2492-6660 en_US http://dx.doi.org/10.1109/IROS.2014.6942559 Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Paull, Liam Seto, Mae Leonard, John Joseph Decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| title | Decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| title_full | Decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| title_fullStr | Decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| title_full_unstemmed | Decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| title_short | Decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| title_sort | decentralized cooperative trajectory estimation for autonomous underwater vehicles |
| url | http://hdl.handle.net/1721.1/97580 https://orcid.org/0000-0002-8863-6550 https://orcid.org/0000-0003-2492-6660 |
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