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Extraterrestrial resource prospectin...
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Johnson, Michael.
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Extraterrestrial resource prospecting using particle swarm optimization and conflict based search.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Extraterrestrial resource prospecting using particle swarm optimization and conflict based search./
作者:
Johnson, Michael.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:
128 p.
附註:
Source: Masters Abstracts International, Volume: 56-03.
Contained By:
Masters Abstracts International56-03(E).
標題:
Mechanical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10250995
ISBN:
9781369515718
Extraterrestrial resource prospecting using particle swarm optimization and conflict based search.
Johnson, Michael.
Extraterrestrial resource prospecting using particle swarm optimization and conflict based search.
- Ann Arbor : ProQuest Dissertations & Theses, 2016 - 128 p.
Source: Masters Abstracts International, Volume: 56-03.
Thesis (M.S.)--University of New Hampshire, 2016.
Extraterrestrial prospecting missions will be an important first step in preparation of the harvesting and extraction of natural resources in space. Additionally, knowledge from a prospecting mission will allow the subsequent harvesting or mining mission to efficiently extract resources. Due to the risk and cost of manned space-flight, these prospecting missions will be robotic in nature, and will enable investors to weigh risk and return of a harvesting mission.
ISBN: 9781369515718Subjects--Topical Terms:
649730
Mechanical engineering.
Extraterrestrial resource prospecting using particle swarm optimization and conflict based search.
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Extraterrestrial prospecting missions will be an important first step in preparation of the harvesting and extraction of natural resources in space. Additionally, knowledge from a prospecting mission will allow the subsequent harvesting or mining mission to efficiently extract resources. Due to the risk and cost of manned space-flight, these prospecting missions will be robotic in nature, and will enable investors to weigh risk and return of a harvesting mission.
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The research presented here explores a hypothetical automated prospecting mission on the Martian surface using landscapes simulated from the Mars Global Surveyor satellite. In this mission, a swarm of autonomous exploration rovers have been sent to the Martian surface to cooperatively search for some natural resource. This study examines optimization and path planning algorithms that would enable such an autonomous mission to accomplish its goals. (Abstract shortened by ProQuest.).
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