Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

  • Sultan Albarakati

Student thesis: Doctoral Thesis


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.
Date of AwardAug 30 2020
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Science and Engineering
SupervisorOmar Knio (Supervisor) & Jeff Shamma (Supervisor)


  • Trajectory planning
  • Autonomous underwater vehicle
  • Multi-objective optimization
  • Risk-aware formulation

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