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Kaffa, Lauren (author)
Loss of control (LOC) is the primary cause of failure of Unmanned Aerial Vehicles (UAV). The safety of these systems can be largely improved by facilitating techniques to prevent LOC to occur, such as Flight Envelope Protection, enabling controllers to keep the system within the Safe Flight Envelope (SFE).<br/>The aim of this work is to examine...
master thesis 2023
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Solanki, Prashant (author)
Quadcopters are becoming increasingly popular across diverse sectors such as mapping, photography, or surveillance. Since rotor damages occur frequently, it is essential to improve the attitude estimation and thus ultimately the ability to control a damaged quadcopter. The Control and Simulation group of TU Delft developed a quadcopter...
master thesis 2020
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Bello, Riccardo (author)
The demand of adding fault tolerance to quadcopter control systems has significantly increased with the rise of adoption of UAVs in numerous sectors. This work proposes and demonstrates the use of Hierarchical Reinforcement Learning to control a quadcopter subject to severe actuator fault. State-of-the-art algorithms are implemented, and a...
master thesis 2021
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van der Velde, Geert (author)
For increasing the safety of quadcopters the development of recovery control algorithms is crucial. A common cause of quadcopter crashes is collisions. To validate recovery control algorithms on collisions, the quadcopter has to reach the post-collision state. <br/>For this, the principle of 'endpoint control' is introduced, bringing the...
master thesis 2023
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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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Altena, Anique (author)
Loss-of-control (LOC) is the main cause of crashes for drones. On-board prevention systems should be designed that require low computing power and memory. Data-driven techniques serve as a solution. This study proposes the use of recurrent neural networks (RNN) for LOC prediction. The aim is to identify which RNN model is most suitable and if...
master thesis 2022
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Ledzian, Patrick (author)
Decentralized control and estimation are both active research areas in the field of systems and control. A new approach to these topics utilizes graph theory to characterize inter-agent communication as a graph that, in this thesis, can have time-varying topology. This approach has been named "network-decentralized" and the use of network...
master thesis 2019
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Kemmeren, Max (author)
As the application areas of Unmanned Aerial Vehicles (UAVs) keep expanding, new flight areas are encountered more often. Small UAVs, named Micro Air Vehicles (MAVs), even fly in areas like sewage pipes. These areas introduce new difficulties such as aerodynamic effects caused by the ground and/or ceiling. In this paper two main contributions are...
master thesis 2021
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Vellekoop, Joris (author)
Deep reinforcement learning presents a compelling approach for the exploration of cluttered 3D environments, offering a balance between fast computation and effective vision-based navigation. Yet, the use of 3D navigation for learning-based information gathering remains largely unexplored. Navigation in 3D space poses the challenge of having an...
master thesis 2024
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