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    Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

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    Type
    Dissertation
    Authors
    Albarakati, Sultan cc
    Advisors
    Knio, Omar cc
    Shamma, Jeff S. cc
    Committee members
    Hoteit, Ibrahim cc
    Lermusiaux, Pierre F.J.
    Program
    Applied Mathematics and Computational Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-08-30
    Permanent link to this record
    http://hdl.handle.net/10754/664897
    
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    Abstract
    In this dissertation, we develop a new framework for 3D trajectory planning of Autonomous Underwater Vehicles (AUVs) in realistic ocean scenarios. The work is divided into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. We apply a Non-Linear Programming (NLP) to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. To assess the effectiveness of the extended framework, we initially test the methodology in idealized settings. The scheme is then demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we account for uncertainty in the ocean current field, is described by an ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. We formulate stochastic problems that aim to minimize a risk measure of the travel time or energy consumption, using a flexible methodology that enables the user to explore various objectives, ranging seamlessly from risk-neutral to risk-averse. The capabilities of the approach are demonstrated using steady and transient currents. Advanced visualization tools have been further designed to simulate results.
    Citation
    Albarakati, S. (2020). Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach. KAUST Research Repository. https://doi.org/10.25781/KAUST-C5D06
    DOI
    10.25781/KAUST-C5D06
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-C5D06
    Scopus Count
    Collections
    Applied Mathematics and Computational Science Program; Dissertations; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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