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Found 16 papers, 2 papers with code
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike
We use deep reinforcement learning (RL) to optimize a weapons to target assignment (WTA) policy for multi-vehicle hypersonic strike against multiple targets.
Extracting Space Situational Awareness Events from News Text
Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes.
Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker
We develop an integrated guidance and control system that in conjunction with a stabilized seeker and landing site detection software can achieve precise and safe planetary landing.
VisualEnv: visual Gym environments with Blender
In this paper VisualEnv, a new tool for creating visual environment for reinforcement learning is introduced.
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning
An adaptive guidance system suitable for the terminal phase trajectory of a hypersonic strike weapon is optimized using reinforcement meta learning.
Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning
We apply a reinforcement meta-learning framework to optimize an integrated and adaptive guidance and flight control system for an air-to-air missile.
Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning
We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle.
Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations
The proposed method is a synergy of two recently developed frameworks for solving problems involving parametric DEs, 1) the Theory of Functional Connections, TFC, and the Physics-Informed Neural Networks, PINN.
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.
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
