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dc.contributor.authorMu, Beipeng
dc.contributor.authorAgha-mohammadi, Ali-akbar
dc.contributor.authorPaull, Liam
dc.contributor.authorGraham, Matthew
dc.contributor.authorHow, Jonathan P.
dc.contributor.authorLeonard, John Joseph
dc.date.accessioned2015-07-29T13:15:39Z
dc.date.available2015-07-29T13:15:39Z
dc.date.issued2015-07
dc.identifier.issn2330-765X
dc.identifier.urihttp://hdl.handle.net/1721.1/97917
dc.description.abstractLong-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot.en_US
dc.description.sponsorshipUnited States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0391)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-11-1-0688)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Award IIS-1318392)en_US
dc.language.isoen_US
dc.relation.isversionofhttp://www.roboticsproceedings.org/rss11/p04.htmlen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceRobotics Proceedingsen_US
dc.titleTwo-Stage Focused Inference for Resource-Constrained Collision-Free Navigationen_US
dc.typeArticleen_US
dc.identifier.citationMu, Beipeng, Ali-akbar Agha-mohammadi, Liam Paull, Matthew Graham, Jonathan How, John Leonard. "Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation." 2015 Robotics: Science and Systems Conference (July 2015).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.mitauthorMu, Beipengen_US
dc.contributor.mitauthorAgha-mohammadi, Ali-akbaren_US
dc.contributor.mitauthorPaull, Liamen_US
dc.contributor.mitauthorGraham, Matthewen_US
dc.contributor.mitauthorHow, Jonathan P.en_US
dc.contributor.mitauthorLeonard, John Josephen_US
dc.relation.journalProceedings of the 2015 Robotics: Science and Systems Conferenceen_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
dspace.orderedauthorsMu, Beipeng; Agha-mohammadi, Ali-akbar; Paull, Liam; Graham, Mathew; How, Jonathan; Leonard, Johnen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8438-7668
dc.identifier.orcidhttps://orcid.org/0000-0002-8863-6550
dc.identifier.orcidhttps://orcid.org/0000-0001-8576-1930
dc.identifier.orcidhttps://orcid.org/0000-0003-2492-6660
mit.licenseOPEN_ACCESS_POLICYen_US


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