This page is dedicated to my Master's Thesis project. The aim of the project is to apply Machine Learning algorithms to enhance the development and usage of a lightweight Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV).

The subtasks of the project can be listed as following:

• Developing an Evolutionary Multi-Objective Bayesian Optimization system.
• Create a Pareto non-dominated vector of Bezier curve based airfoils.
• Use the non-dominated airfoils in coeffect with the bodies global parameters (e.g span, taper and sweep) to find a global approximate optimal solution with a Panel Method based system.
• Find a local optimal solution using OpenFOAM/SU2 based Computational Fluid Dynamics softare.
• Test optimized wing.

• Parametrize propulsion and control surface system to project requirements.
• Set up Raspberry Pi based autopilot system.
• Test autopilot.

• Set up a Deep Q Learning (or other) based reinforcement training policy.
• Initial data collection of operator controlling vertical hovering and VTOL of the BWB with a RC control.
• Controlled environment training of vertical hovering and VTOL.
• Outdoor environment training of VTOL and tests.

• Set up camera and telemetry system for BB UAV.
• Set up software to recognize a 2m x 2m blanket as target for landing or package drop.

• Set up communications with EPOC EMOTIV

• Set up neural data transformation pipeline

• Benchmark classification algorithms with crossvalidation:

  a) Hidden Markov Models (HMM)

  b) Ensemble Methods (EM)

  c) Multilayer Perceptrons (MLP)

• Choose and combine (if necessary) classification algorithms

• Test BCI system

• Set up communications with BWB UAV

• Set up transformation and classification pipeline on UAV onboard computer

Progress so far:

For instructionss on how to set up an eGPU eGPU on Dell.