Image-based Deep Reinforcement Learning for Autonomous Lunar Landing
Future missions to the Moon and Mars will require advanced guidance navigation and control algorithms for the powered descent phase. These algorithm should be capable of reconstructing the state of the spacecraft using the inputs from an array of sensors and apply the required command to ensure pinp...
Main Authors: | Scorsoglio, Andrea (Author), Furfaro, Roberto (Author), Linares, Richard (Author), Gaudet, Brian (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
American Institute of Aeronautics and Astronautics (AIAA),
2021-11-08T18:04:09Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Deep reinforcement learning for six degree-of-freedom planetary landing
by: Gaudet, Brian, et al.
Published: (2021) -
A Guidance Law for Terminal Phase Exo-Atmospheric Interception Against a Maneuvering Target using Angle-Only Measurements Optimized using Reinforcement Meta-Learning
by: Gaudet, Brian, et al.
Published: (2022) -
UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy
by: Jingyi Xie, et al.
Published: (2020-10-01) -
End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control
by: Jiang Zhao, et al.
Published: (2021-09-01) -
DIDO optimization of a lunar landing trajectory with respect to autonomous landing hazard avoidance technology
by: Francis, Michael R.
Published: (2012)