optimization

Chaabane Mankai

M.S. Student, Electrical and Computer Engineering

Wireless Communications optimization Performance analysis network security

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab

artificial intelligence TinyML optimization deep learning

Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

Sultan Albarakati, Director, KAUST Academy

Jun 7, 16:00 - 18:00

KAUST

In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work 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. The latter are used to specify both the ocean current and the bathymetry. We apply a 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. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite 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. Advanced visualization tools are used to amplify simulation results.

Ricardo Manuel Pinto de Lima

Research Scientist, Applied Mathematics and Computational Sciences

optimization process systems engineering renewable energy energy systems stochastic programming robust optimization uncertainty quantification risk management nlp minlp milp scheduling

My mission is to develop computational models and methods based on mathematical optimization and data science for sustainable energy and chemical processes. I am deeply committed to decarbonization, climate change mitigation, and optimal operations management.