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Full-Text Articles in Engineering
High-Fidelity 3d Reconstruction Of Space Bodies Using Machine Learning And Neural Radiance Fields, Timothy Jacob Huber
High-Fidelity 3d Reconstruction Of Space Bodies Using Machine Learning And Neural Radiance Fields, Timothy Jacob Huber
Theses and Dissertations
In the era of burgeoning space exploration, the growing population of spacecraft heightens the inevitability of collisions. While traditional imagery remains effective for damage assessment, its lack of 3D representation of the object necessitates more advanced approaches. This research delves into cutting-edge methodologies, with a primary focus on leveraging machine learning and computer vision technologies to enhance the precision and efficiency of damage assessment by taking imagery of a space body and creating a 3D model. The study extensively investigates TransMVSNet, a neural network code employing classical computer vision techniques such as multi-vision stereo (MVS) and depth maps. This approach …
Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko
Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko
Doctoral Dissertations
“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …
Optimal And Robust Neural Network Controllers For Proximal Spacecraft Maneuvers, B. Cole George
Optimal And Robust Neural Network Controllers For Proximal Spacecraft Maneuvers, B. Cole George
Theses and Dissertations
Recent successes in machine learning research, buoyed by advances in computational power, have revitalized interest in neural networks and demonstrated their potential in solving complex controls problems. In this research, the reinforcement learning framework is combined with traditional direct shooting methods to generate optimal proximal spacecraft maneuvers. Open-loop and closed-loop feedback controllers, parameterized by multi-layer feed-forward artificial neural networks, are developed with evolutionary and gradient-based optimization algorithms. Utilizing Clohessy- Wiltshire relative motion dynamics, terminally constrained fixed-time, fuel-optimal trajectories are solved for intercept, rendezvous, and natural motion circumnavigation transfer maneuvers using three different thrust models: impulsive, finite, and continuous. In addition …
Application Of Spectral Solution And Neural Network Techniques In Plasma Modeling For Electric Propulsion, Joseph R. Whitman
Application Of Spectral Solution And Neural Network Techniques In Plasma Modeling For Electric Propulsion, Joseph R. Whitman
Theses and Dissertations
A solver for Poisson's equation was developed using the Radix-2 FFT method first invented by Carl Friedrich Gauss. Its performance was characterized using simulated data and identical boundary conditions to those found in a Hall Effect Thruster. The characterization showed errors below machine-zero with noise-free data, and above 20% noise-to-signal strength, the error increased linearly with the noise. This solver can be implemented into AFRL's plasma simulator, the Thermophysics Universal Research Framework (TURF) and used to quickly and accurately compute the electric field based on charge distributions. The validity of a machine learning approach and data-based complex system modeling approach …
The Hilbert-Huang Transform: A Theoretical Framework And Applications To Leak Identification In Pressurized Space Modules, Kenneth R. Bundy
The Hilbert-Huang Transform: A Theoretical Framework And Applications To Leak Identification In Pressurized Space Modules, Kenneth R. Bundy
Electronic Theses and Dissertations
Any manned space mission must provide breathable air to its crew. For this reason, air leaks in spacecraft pose a danger to the mission and any astronauts on board. The purpose of this work is twofold: the first is to address the issue of air pressure loss from leaks in spacecraft. Air leaks present a danger to spacecraft crew, and so a method of finding air leaks when they occur is needed. Most leak detection systems localize the leak in some way. Instead, we address the identification of air leaks in a pressurized space module, we aim to determine the …
Atmospheric Entry, Dillon A. Martin
Atmospheric Entry, Dillon A. Martin
Honors Undergraduate Theses
The development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and compared to the Apollo case.