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Active Path Planning for Drones in O...

Wang, Zeyangyi.

Active Path Planning for Drones in Object Search.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Active Path Planning for Drones in Object Search./
Author:	Wang, Zeyangyi.
Description:	1 online resource (24 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355660302

Active Path Planning for Drones in Object Search.
Wang, Zeyangyi.

Active Path Planning for Drones in Object Search. - 1 online resource (24 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--University of California, San Diego, 2017.

Includes bibliographical references

Object searching is one of the most popular applications of unmanned aerial vehicles. Low cost small drones are particularly suited for surveying tasks in difficult conditions. With their limited on-board processing power and battery life, there is a need for more efficient search algorithm. The proposed path planning algorithm utilizes AZ-net, a deep learning network to process images captured on drones for adaptive flight path planning. Search simulation based on videos and actual experiments show significant reduction in search time under certain circumstances, compared to traditional linear search method. The thesis will discuss important design tradeoff between performance and battery life.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355660302Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Active Path Planning for Drones in Object Search.
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006 m o u
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008 190606s2017 xx obm 000 0 eng d
020 $a 9780355660302
035 $a (MiAaPQ)AAI10686884
035 $a (MiAaPQ)ucsd:17105
035 $a AAI10686884
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Wang, Zeyangyi. $3 1193439
245 1 0 $a Active Path Planning for Drones in Object Search.
264 0 $c 2017
300 $a 1 online resource (24 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Tara Javidi.
502 $a Thesis (M.S.)--University of California, San Diego, 2017.
504 $a Includes bibliographical references
520 $a Object searching is one of the most popular applications of unmanned aerial vehicles. Low cost small drones are particularly suited for surveying tasks in difficult conditions. With their limited on-board processing power and battery life, there is a need for more efficient search algorithm. The proposed path planning algorithm utilizes AZ-net, a deep learning network to process images captured on drones for adaptive flight path planning. Search simulation based on videos and actual experiments show significant reduction in search time under certain circumstances, compared to traditional linear search method. The thesis will discuss important design tradeoff between performance and battery life.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Electrical Engineering. $3 1184434
773 0 $t Masters Abstracts International $g 57-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10686884 $z click for full text (PQDT)

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