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Found 14 papers, 4 papers with code
Language Models are Spacecraft Operators
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts.
Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions
With the rapid increase in the number of Anthropogenic Space Objects (ASOs), Low Earth Orbit (LEO) is facing significant congestion, thereby posing challenges to space operators and risking the viability of the space environment for varied uses.
Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction
T-PDG uses data from prior runs of trajectory optimization algorithms to train a transformer neural network, which accurately predicts the relationship between problem parameters and the globally optimal solution for the powered descent guidance problem.
Transformer-based Atmospheric Density Forecasting
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for atmospheric density forecasting are vital for space situational awareness.
Approximate Analytical Solution to the Zonal Harmonics Problem Using Koopman Operator Theory
This work introduces the use of the Koopman operator theory to generate approximate analytical solutions for the zonal harmonics problem of a satellite orbiting a non-spherical celestial body.
Earth and Planetary Astrophysics Functional Analysis Space Physics
Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop
We use Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target.
Six Degree-of-Freedom Body-Fixed Hovering over Unmapped Asteroids via LIDAR Altimetry and Reinforcement Meta-Learning
This allows the deployed policy to generalize well to novel asteroid characteristics, which we demonstrate in our experiments.
Real-Time Thermospheric Density Estimation Via Two-Line-Element Data Assimilation
Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction.
Space Physics Earth and Planetary Astrophysics Dynamical Systems Atmospheric and Oceanic Physics
Seeker based Adaptive Guidance via Reinforcement Meta-Learning Applied to Asteroid Close Proximity Operations
Finally, we suggest a concept of operations for asteroid close proximity maneuvers that is compatible with the guidance system.
Reinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets
We present a novel guidance law that uses observations consisting solely of seeker line of sight angle measurements and their rate of change.
Systems and Control
Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning
We also demonstrate the ability of a RL meta-learning optimized policy to implement a guidance law using observations consisting of only Doppler radar altimeter readings in a Mars landing environment, and LIDAR altimeter readings in an asteroid landing environment, thus integrating guidance and navigation.
Systems and Control
Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
Instead, we learn a mapping from the first observation in an episode to the hidden state, allowing the trained model to immediately produce accurate predictions.
Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landing
The latter requires both a navigation system capable of estimating the lander's state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine.
Systems and Control
Random Finite Set Theory and Centralized Control of Large Collaborative Swarms
Additionally, the RFS control formulation is shown to be very flexible in terms of the number of agents in the swarm and configuration of the desired Gaussian mixtures.
Systems and Control
