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Bink, Kiki (author)
Facing the critical challenge of reducing greenhouse gas (GHG) emissions in the maritime industry, this thesis explores the potential of smart control systems using Reinforcement Learning (RL) for autonomous sailing. Traditional controls for sailing fall short in navigating the complex, dynamic conditions of maritime environments. RL has shown...
master thesis 2024
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van den Berg, Jasper (author)
The traumatic loss of a hand is a horrific experience usually followed by significant psychological, functional and rehabilitation challenges. Even though much progress has been made in the past decades, the prosthetic challenge of restoring the human hand functionality is still far from being achieved. Autonomous prosthetic hands showed...
master thesis 2023
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Vieira dos Santos, Lucas (author)
The critical challenge for employing autonomous control systems in aircraft is ensuring robustness and safety. This study introduces an intelligent and fault-tolerant controller that merges two Reinforcement Learning (RL) algorithms in a hybrid approach: the Distributional Soft Actor-Critic (DSAC) and the Incremental Dual Heuristic Programming ...
master thesis 2023
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Probst, Johanna (author)
Creating autonomous Micro Aerial Vehicles for executing complex missions poses various challenges, including safe navigation in the presence of external wind disturbances. Most current navigation methods handle external wind disturbances through real-time estimation and rejection algorithms in the control stage, but lack safety guarantees in...
master thesis 2023
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Seres, Peter (author)
With the recent increase in the complexity of aerospace systems and autonomous operations, there is a need for an increased level of adaptability and model-free controller synthesis. Such operations require the controller to maintain safety and performance without human intervention in non-static environments with partial observability and...
master thesis 2022
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de Lange, Rudy (author)
This thesis proposes the novel Behaviour Tree Update Framework (BTUF) for the initial construction and continuous incremental adaptation of Behaviour Trees (BTs) for applications in Learning from Demonstration (LfD) frameworks to create complex robot behaviours associated with Activities of Daily Living (ADL) without requiring the user to have a...
master thesis 2022
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van Zeumeren, Ilse (author)
This project is focussed on a design for stimulating interaction amongst passengers in the Seabubble. The Seabubble is an autonomous hydrofoil that is able to fly above water. The Seabubble can transport 4 to 5 passengers on inland waterways. The hydrofoil-technology creates a whole new travel-experience because it provides very stable and...
master thesis 2020
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Fris, Rein (author)
Deep Reinforcement Learning (DRL) enables us to design controllers for complex tasks with a deep learning approach. It allows us to design controllers that are otherwise cumbersome to design with conventional control methodologies. Often, an objective for RL is binary in nature. However, exploring in environments with sparse rewards is a problem...
master thesis 2020
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Vonk, Bart (author)
Research on reinforcement learning algorithms to play complex video games have brought forth controllers surpassing human performance. This paper explores the possibilities of applying these techniques to the sequencing and spacing of aircraft. Two experiments are performed. First a single aircraft must learn to fly a 4D trajectory using only...
master thesis 2019
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