Show simple item record

dc.contributor.advisorBrian Williams.en_US
dc.contributor.authorBush, Lawrence A. Men_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Aeronautics and Astronautics.en_US
dc.date.accessioned2014-03-19T15:43:21Z
dc.date.available2014-03-19T15:43:21Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/85758
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 272-283).en_US
dc.description.abstractOver the past several decades, technologies for remote sensing and exploration have become increasingly powerful but continue to face limitations in the areas of information gathering and analysis. These limitations affect technologies that use autonomous agents, which are devices that can make routine decisions independent of operator instructions. Bandwidth and other communications limitation require that autonomous differentiate between relevant and irrelevant information in a computationally efficient manner. This thesis presents a novel approach to this problem by framing it as an adaptive sensing problem. Adaptive sensing allows agents to modify their information collection strategies in response to the information gathered in real time. We developed and tested optimization algorithms that apply information guides to Monte Carlo planners. Information guides provide a mechanism by which the algorithms may blend online (realtime) and offline (previously simulated) planning in order to incorporate uncertainty into the decisionmaking process. This greatly reduces computational operations as well as decisional and communications overhead. We begin by introducing a 3-level hierarchy that visualizes adaptive sensing at synoptic (global), mesocale (intermediate) and microscale (close-up) levels (a spatial hierarchy). We then introduce new algorithms for decision uncertainty minimization (DUM) and representational uncertainty minimization (RUM). Finally, we demonstrate the utility of this approach to real-world sensing problems, including bathymetric mapping and disaster relief. We also examine its potential in space exploration tasks by describing its use in a hypothetical aerial exploration of Mars. Our ultimate goal is to facilitate future large-scale missions to extraterrestrial objects for the purposes of scientific advancement and human exploration.en_US
dc.description.statementofresponsibilityby Lawrence A. M. Bush.en_US
dc.format.extent310 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectAeronautics and Astronautics.en_US
dc.titleDecision uncertainty minimization and autonomous information gatheringen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
dc.identifier.oclc871249335en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record