Safe Lunar landing via images: A Reinforcement Meta-Learning application to autonomous hazard avoidance and landing
Ground view, segmentation mask ...
- Reinforcement Learning 5
- Physics Informed Neural Networks 3
- Meta-Reinforcement Learning 1
- Computer Vision 1
Reinforcement Learning
Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach
Precision landing on large and small planetary bodies is a techno...
Image-based deep reinforcement learning for autonomous lunar landing
Digital terrain model and rendered view Future missions to...
ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbit
Trajectory and Lyapunov vector field In this paper, we present a new feedback guidance algorithm f...
Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit
Trajectories This paper aims a developing a new feedback gui...
Physics Informed Neural Networks
Orbit determination via physics informed neural networks
Estimated trajectories This paper introduces a method for solving orbit determination problems named Physics Informed Orbit Determinatio...
Orbit determination via physics informed neural networks in cislunar environment
Estimated trajectories The approach presented in this paper is based on using Physics Informed Ne...
Orbit determination via physics informed neural networks
Estimated trajectories ...
Meta-Reinforcement Learning
Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing
Future exploration and human missions on large planetary bodies (e.g., moon, Mars) will require advanced g...
Computer Vision
VisualEnv: visual Gym environments with Blender
VisualEnv is a tool designed for creating visual environment for reinforcement learning. It is the product of an integration of an open-source modelling an...