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dc.contributor.advisorCésar A. Hidalgo.en_US
dc.contributor.authorPetkova, Miaen_US
dc.contributor.otherProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.date.accessioned2016-12-22T16:26:52Z
dc.date.available2016-12-22T16:26:52Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/106049
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 57-61).en_US
dc.description.abstractRoad repair expenditure comprises a significant portion of US federal and municipal budgets. Inspection and timely maintenance are crucial preventative measures against pavement distress formation that can lower the monetary burden of repairs. Yet state of the art road inspection techniques still employ technicians to perform distress measurements manually. These methods are often too costly, time-consuming, labor-intensive and require technical expertise. Meanwhile, autonomous systems are increasingly deployed in place of human operators where tasks are monotonous and where risk of exposure to hostile conditions is great. As a time-consuming but highly repetitive task, road inspection presents a promising candidate for task automation. Automating road inspection can present significant efficiency gains that can aid agencies in responding to early signs of erosion in a timely manner. In this work, I explore the capacity of drones to perform autonomous pavement inspections. I develop a system that dispatches drones to survey an area, diagnose the presence of pavement distress in real time, and record imagery and coordinates of locations requiring repair. This system presents an alternative to on-ground inspections and tools that draw on crowd-sourced mechanisms to identify potholes. It builds on other recent technological solutions that employ remote sensing to collect and interpret data on pavement health. The results from this mission will be visualized through a web platform that can not only aid cities in consolidating a fragmented and costly data collection process, but also in minimize human error in the identification and prioritization of problem areas.en_US
dc.description.statementofresponsibilityby Mia Petkova.en_US
dc.format.extent61 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.subjectProgram in Media Arts and Sciences ()en_US
dc.titleDeploying drones for autonomous detection of pavement distressen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.identifier.oclc964698554en_US


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