NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Machine Learning For Planetary Mining ApplicationsRobotic mining could prove to be an efficient method of mining resources for extended missions on the Moon or Mars. One component of robotic mining is scouting an area for resources to be mined by other robotic systems. Writing controllers for scouting can be difficult due to the need for fault tolerance, inter-agent cooperation, and agent problem solving. Reinforcement learning could solve these problems by enabling the scouts to learn to improve their performance over time. This work is divided into two sections, with each section addressing the use of machine learning in this domain. The first contribution of this work focuses on the application of reinforcement learning to mining mission analysis. Various mission parameters were modified and control policies were learned. Then agent performance was used to assess the effect of the mission parameters on the performance of the mission. The second contribution of this work explores the potential use of reinforcement learning to learn a controller for the scouts. Through learning, these scouts would improve their ability to map their surroundings over time.
Document ID
20200000703
Acquisition Source
Langley Research Center
Document Type
Technical Memorandum (TM)
Authors
Cook, Joshua
(Oregon State Univ. Corvallis, OR, United States)
Samareh, Jamshid
(NASA Langley Research Center Hampton, VA, United States)
Date Acquired
February 4, 2020
Publication Date
January 1, 2020
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
NF1676L-35451
NASA/TM-2020-220439
Funding Number(s)
WBS: 954879.02.01.23.01
Distribution Limits
Public
Copyright
Public Use Permitted.
No Preview Available