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dc.contributor.advisorGabriel, Steven A.en_US
dc.contributor.authorQuigley, Kevin Jen_US
dc.date.accessioned2019-06-22T05:42:49Z
dc.date.available2019-06-22T05:42:49Z
dc.date.issued2019en_US
dc.identifierhttps://doi.org/10.13016/ybuq-bhgb
dc.identifier.urihttp://hdl.handle.net/1903/22233
dc.description.abstractAutonomy in unmanned underwater vehicle (UUV) navigation is critical for most applications due to inability of human operators to control, monitor or intervene in underwater environments. To ensure safe autonomous navigation, verification and validation (V&V) procedures are needed for various applications. This thesis proposes a game theory-based benchmark validation technique for trajectory optimization for non-cooperative UUVs. A quadratically constrained nonlinear program formulation is presented, and a "perfect-information reality" validation framework is derived by finding a Nash equilibrium to various two-player pursuit-evasion games (PEG). A Karush-Kuhn-Tucker (KKT) point to such a game represents a best-case local optimum, given perfect information available to non-cooperative agents. Rolling-horizon foresight with robust obstacles are incorporated to demonstrate incomplete information and stochastic environmental conditions. A MATLAB-GAMS interface is developed to model the rolling-horizon game, and is solved via a mixed complementarity problem (MCP), and illustrative examples show how equilibrium trajectories can serve as benchmarks for more practical real-time path planners.en_US
dc.language.isoenen_US
dc.titleMULTI-AGENT UNMANNED UNDERWATER VEHICLE VALIDATION VIA ROLLING-HORIZON ROBUST GAMESen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.subject.pqcontrolledOperations researchen_US
dc.subject.pquncontrolledgame theoryen_US
dc.subject.pquncontrolledmixed complementarity problemsen_US
dc.subject.pquncontrolledoptimizationen_US
dc.subject.pquncontrolledrobustnessen_US
dc.subject.pquncontrolledUUVen_US
dc.subject.pquncontrolledV&Ven_US


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