Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing-Reference-Cited by-同舟云学术

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Published:2022-11 Issue:6 Volume:59 Page:1800-1808
ISSN:0022-4650
Container-title:Journal of Spacecraft and Rockets
language:en
Short-container-title:Journal of Spacecraft and Rockets

Author:

Tomita Kento¹,Skinner Katherine A.²,Ho Koki¹

Affiliation:

1. Georgia Institute of Technology, Atlanta, Georgia 30332

2. University of Michigan, Ann Arbor, Michigan 48109

Funder

National Aeronautics and Space Administration

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Space and Planetary Science,Aerospace Engineering

Link

https://arc.aiaa.org/doi/pdf/10.2514/1.A35104

Reference25 articles.

1. MustardJ.AdlerM.AllwoodA.BassD.BeatyD.BellJ.BrinckerhoffW.CarrM.Des MaraisD.BrakeB.et al. “Report of the Mars 2020 Science Definition Team,” Mars Exploration Program Analysis Group, July 2013.

2. In-flight experience of the Mars Science Laboratory Guidance, Navigation, and Control system for Entry, Descent, and Landing

3. Free-Flight Terrestrial Rocket Lander Demonstration for NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System

4. Flight testing a Real-Time Hazard Detection System for Safe Lunar Landing on the Rocket-Powered Morpheus Vehicle

5. Guidance Summary and Assessment of the Chang’e-3 Powered Descent and Landing

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