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Articles 1 - 4 of 4
Full-Text Articles in Astrodynamics
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Doctoral Dissertations and Master's Theses
This study presents a reinforcement learning (RL) approach for reestablishing communication with deep-space satellites under unknown attitude determination and control system (ADCS) failures. When traditional fault-tolerant control methods cannot restore signal, the proposed RL controller acts as a last-resort measure by autonomously reorienting the satellite’s antenna toward Earth while charging the battery via solar panels. A generic reward function, designed for the RL-based method, enables the controller to adapt to diverse failure scenarios, including severe actuator noise, misalignment, and complete actuator failure. Simulations are conducted in the Basilisk environment and trained with the tonic framework and demonstrate ranging capabilities of …
Using Machine Learning To Predict Hypervelocity Fragment Propagation Of Space Debris Collisions, Katharine Larsen, Riccardo Bevilacqua
Using Machine Learning To Predict Hypervelocity Fragment Propagation Of Space Debris Collisions, Katharine Larsen, Riccardo Bevilacqua
Student Works
The future of spaceflight is threatened by the increasing amount of space debris, especially in the near-Earth environment. To continue operations, accurate characterization of hypervelocity fragment propagation following collisions and explosions is imperative. While large debris particles can be tracked by current methods, small particles are often missed. This paper presents a method to estimate fragment fly-out properties, such as fragment, velocity, and mass distributions, using machine learning. Previous work was performed on terrestrial data and associated simulations representing space debris collisions. The fragmentation of high-velocity fragmentation can be modeled by terrestrial fragmentation tests, such as static detonations. Recently, stereoscopic …
Probabilistic Space Weather Modeling And Forecasting For The Challenge Of Orbital Drag In Space Traffic Management, Richard J. Licata Iii
Probabilistic Space Weather Modeling And Forecasting For The Challenge Of Orbital Drag In Space Traffic Management, Richard J. Licata Iii
Graduate Theses, Dissertations, and Problem Reports (ETD)
In the modern space age, private companies are crowding the already-congested low Earth orbit (LEO) regime with small satellite mega constellations. With over 25,000 objects larger than 10 cm already in LEO, this rapid expansion is forcing us towards the enterprise on Space Traffic Management (STM). STM is an operational effort that focuses on conjunction assessment and collision avoidance between objects. While the equations of motion for objects in orbit are well-known, there are many uncertain parameters that result in the uncertainty of an object's future position. The force that the atmosphere exerts on satellite - known as drag - …
Generating Exploration Mission-3 Trajectories To A 9:2 Nrho Using Machine Learning, Esteban Guzman
Generating Exploration Mission-3 Trajectories To A 9:2 Nrho Using Machine Learning, Esteban Guzman
Master's Theses
The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on …