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dc.contributor.advisorZhang, Fumin
dc.contributor.authorSzwaykowska, Klementyna
dc.date.accessioned2014-01-13T16:49:46Z
dc.date.available2014-01-13T16:49:46Z
dc.date.created2013-12
dc.date.issued2013-11-18
dc.date.submittedDecember 2013
dc.identifier.urihttp://hdl.handle.net/1853/50357
dc.description.abstractUse of model-based path planning and navigation is a common strategy in mobile robotics. However, navigation performance may degrade in complex, time-varying environments under model uncertainty because of loss of prediction ability for the robot state over time. Exploration and monitoring of ocean regions using autonomous marine robots is a prime example of an application where use of environmental models can have great benefits in navigation capability. Yet, in spite of recent improvements in ocean modeling, errors in model-based flow forecasts can still significantly affect the accuracy of predictions of robot positions over time, leading to impaired path-following performance. In developing new autonomous navigation strategies, it is important to have a quantitative understanding of error in predicted robot position under different flow conditions and control strategies. The main contributions of this thesis include development of an analytical model for the growth of error in predicted robot position over time and theoretical derivation of bounds on the error growth, where error can be attributed to drift caused by unmodeled components of ocean flow. Unlike most previous works, this work explicitly includes spatial structure of unmodeled flow components in the proposed error growth model. It is shown that, for a robot operating under flow-canceling control in a static flow field with stochastic errors in flow values returned at ocean model gridpoints, the error growth is initially rapid, but slows when it reaches a value of approximately twice the ocean model gridsize. Theoretical values for mean and variance of error over time under a station-keeping feedback control strategy and time-varying flow fields are computed. Growth of error in predicted vehicle position is modeled for ocean models whose flow forecasts include errors with large spatial scales. Results are verified using data from several extended field deployments of Slocum autonomous underwater gliders, in Monterey Bay, CA in 2006, and in Long Bay, SC in 2012 and 2013.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectStochastic systems
dc.subjectError modeling
dc.subjectUnderwater navigation
dc.subject.lcshUnderwater navigation
dc.subject.lcshError analysis (Mathematics)
dc.subject.lcshMobile robots
dc.titleControlled Lagrangian particle tracking: analyzing the predictability of trajectories of autonomous agents in ocean flows
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberVerriest, Erik
dc.contributor.committeeMemberEgerstedt, Magnus
dc.contributor.committeeMemberEdwards, Catherine R.
dc.contributor.committeeMemberLee, Chin-Hui
dc.date.updated2014-01-13T16:49:46Z


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