miun.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Planning semi-autonomous drone photo missions in Google Earth
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and System science.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This report covers an investigation of the methods and algorithms required to plan and perform semi-autonomous photo missions on Apple iPad devices using data exported from Google Earth. Flight time was to be minimized, taking

wind velocity and aircraft performance into account. Google Earth was used both to define what photos to take, and to define the allowable mission area for the aircraft. A benchmark mission was created containing 30 photo

operations in a 250 by 500 m area containing several no-fly-areas. The report demonstrates that photos taken in Google Earth can be reproduced in reality with good visual resemblance. High quality paths between all possible photo operation pairs in the benchmark mission could be found in seconds using the Theta* algorithm in a 3D grid representation with six-edge connectivity (Up, Down, North, South, East, West). Smoothing the path in a post-processing step was shown to further increase the quality of the path at a very low computational cost. An optimal route between the operations in the benchmark mission, using the paths found

by Theta*, could be found in less than half a minute using a Branch-and-Bound algorithm. It was however also found that prematurely terminating the algorithm after five seconds yielded a route that was close enough to optimal not to warrant running the algorithm to completion.

Place, publisher, year, edition, pages
2017. , p. 61
Keywords [en]
Google Earth, Robotics, Pathfinding, A*, Theta*, Path smoothing, Traveling Salesman Problem, Branch-and-Bound, Swift, C++
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:miun:diva-31473OAI: oai:DiVA.org:miun-31473DiVA, id: diva2:1135549
Subject / course
Computer Engineering DT1
Educational program
Software Engineering TPVAG 120/180 higher education credits
Supervisors
Examiners
Available from: 2017-08-23 Created: 2017-08-23 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(5578 kB)142 downloads
File information
File name FULLTEXT01.pdfFile size 5578 kBChecksum SHA-512
819c7cc2e73c87c09d5f7bf7e76e23afca36fa9051c3a5a13d4b2245ab317a7b7777d0fe63304e9f71fb70b1955ef93e86f11a78114d087b877262a592a0cd17
Type fulltextMimetype application/pdf

By organisation
Department of Computer and System science
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 142 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 613 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf