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dc.contributor.authorPapalia, Alan
dc.contributor.authorLeonard, John
dc.date.accessioned2022-01-10T20:08:10Z
dc.date.available2022-01-07T19:51:19Z
dc.date.available2022-01-10T20:08:10Z
dc.date.issued2020
dc.identifier.urihttps://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.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/AUV50043.2020.9267910en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleNetwork Localization Based Planning for Autonomous Underwater Vehicles with Inter-Vehicle Rangingen_US
dc.typeArticleen_US
dc.identifier.citationPapalia, 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.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journal2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-01-07T19:45:55Z
dspace.orderedauthorsPapalia, A; Leonard, Jen_US
dspace.date.submission2022-01-07T19:45:57Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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