dc.contributor.author | Papalia, Alan | |
dc.contributor.author | Leonard, John | |
dc.date.accessioned | 2022-01-10T20:08:10Z | |
dc.date.available | 2022-01-07T19:51:19Z | |
dc.date.available | 2022-01-10T20:08:10Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/138847.2 | |
dc.description.abstract | © 2020 IEEE. Localization between a swarm of AUVs can be entirely estimated through the use of range measurements between neighboring AUVs via a class of techniques commonly referred to as sensor network localization. However, the localization quality depends on network topology, with degenerate topologies, referred to as low-rigidity configurations, leading to ambiguous or highly uncertain localization results. This paper presents tools for rigidity-based analysis, planning, and control of a multi-AUV network which account for sensor noise and limited sensing range. We evaluate our long-term planning framework in several two-dimensional simulated environments and show we are able to generate paths in feasible time and guarantee a minimum network rigidity over the full course of the paths. | en_US |
dc.language.iso | en | |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | 10.1109/AUV50043.2020.9267910 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | Network Localization Based Planning for Autonomous Underwater Vehicles with Inter-Vehicle Ranging | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Papalia, Alan and Leonard, John. 2020. "Network Localization Based Planning for Autonomous Underwater Vehicles with Inter-Vehicle Ranging." 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.relation.journal | 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020 | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2022-01-07T19:45:55Z | |
dspace.orderedauthors | Papalia, A; Leonard, J | en_US |
dspace.date.submission | 2022-01-07T19:45:57Z | |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Publication Information Needed | en_US |