dc.contributor.author | Ahmed, Nisar | |
dc.contributor.author | Luders, Brandon Douglas | |
dc.contributor.author | Sample, Eric | |
dc.contributor.author | Shah, Danelle | |
dc.contributor.author | Campbell, Mark | |
dc.contributor.author | How, Jonathan P. | |
dc.contributor.author | Ponda, Sameera S. | |
dc.date.accessioned | 2013-11-07T19:27:19Z | |
dc.date.available | 2013-11-07T19:27:19Z | |
dc.date.issued | 2011-08 | |
dc.identifier.isbn | 978-1-60086-952-5 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/82025 | |
dc.description.abstract | This paper introduces a novel planning and estimation framework for maximizing infor-
mation collection in missions involving cooperative teams of multiple autonomous vehicles
and human agents, such as those used for multi-target search and tracking. The main
contribution of this work is the scalable uni cation of e ective algorithms for distributed
high-level task planning, decentralized information-based trajectory planning, and hybrid
Bayesian information fusion through a common Gaussian mixture uncertainty representa-
tion, which can accommodate multiple mission objectives and constraints as well as het-
erogeneous human/robot information sources. The proposed framework is validated with
promising results on real hardware through a set of experiments involving a human-robot
team performing a multi-target search mission. | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Graduate Reserach Fellowship) | en_US |
dc.description.sponsorship | United States. Multidisciplinary University Research Initiative (FA9550-08-1-0356) | en_US |
dc.language.iso | en_US | |
dc.publisher | American Institute of Aeromautics and Astronautics | en_US |
dc.relation.isversionof | http://arc.aiaa.org/doi/pdf/10.2514/6.2011-6238 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike 3.0 | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/ | en_US |
dc.source | MIT web domain | en_US |
dc.title | Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Sameera Ponda, Nisar Ahmed, Brandon Luders, Eric Sample, Tauhira Hoossainy, Danelle Shah, Mark Campbell, and Jonathan How. "Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions" AIAA Guidance, Navigation, and Control Conference. August. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics | en_US |
dc.contributor.mitauthor | Ponda, Sameera S. | en_US |
dc.contributor.mitauthor | Luders, Brandon Douglas | en_US |
dc.contributor.mitauthor | How, Jonathan P. | en_US |
dc.relation.journal | Proceedings of the AIAA Guidance, Navigation, and Control Conference | 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 |
dspace.orderedauthors | Ponda, Sameera; Ahmed, Nisar; Luders, Brandon; Sample, Eric; Hoossainy, Tauhira; Shah, Danelle; Campbell, Mark; How, Jonathan | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-8576-1930 | |
mit.license | OPEN_ACCESS_POLICY | en_US |
mit.metadata.status | Complete | |