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
Formation Control and UAV Path Finding Under Uncertainty: A contingent and cooperative swarm intelligence approach
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2020 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Several of our technological breakthroughs are influenced by types of behavior and structures developed in the natural world, including the emulation of swarm in- telligence and the engineering of artificial synapses that function like the human mind. Much like these breakthroughs, this report examines emerging behaviors across swarms of non-communicating, adaptive units that evade obstacles while find- ing a path, to present a swarming algorithm premised on a class of local rule sets re- sulting in a Unmanned Aerial Vehicle (UAV) group navigating together as a unified swarm. Primarily, this method’s important quality is that its rules are local in nature. Thus, the exponential calculations which can be supposed with growing number of drones, their states, and potential tasks are remedied. To this extent, the study tests the algorithmic rules in experiments to replicate the desired behavior in a bounded virtual space filled with simulated units. Simultaneously, in the adaptation of natural flocking rules the study also introduces the rule sets for goal seeking and uncertainty evasion. In effect, the study succeeds in reaching and displaying the desired goals even as the units avoid unknown before flight obstacles and inter-unit collisions with- out the need for a global centralized command nor a leader based hierarchical system.

Place, publisher, year, edition, pages
2020. , p. 46
Keywords [en]
Emergence, Swarm Intelligence, Cooperation, Consensus, Path Find- ing, Uncertainty, Obstacle avoidance, Unknown Beforehand, Decision making, Flocking behavior, Cohesion, Separation, Alignment, Steering, Pattern forma- tion, Self-organization, Swarm, UAV, Drone
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-91379OAI: oai:DiVA.org:lnu-91379DiVA, id: diva2:1389111
Educational program
Software Technology Programme, 180 credits
Supervisors
Available from: 2020-01-29 Created: 2020-01-28 Last updated: 2020-01-29Bibliographically approved

Open Access in DiVA

fulltext(2156 kB)23 downloads
File information
File name FULLTEXT01.pdfFile size 2156 kBChecksum SHA-512
9ed56789dcbb9cd92b603cdda07fa00eefa5a6dfaea00081632e0af2f27e7786995f853bf12d4c2aa05c615fd797502ac2f7b7f8a30aeb5fca8bdd579d6e62f6
Type fulltextMimetype application/pdf

By organisation
Department of computer science and media technology (CM)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 23 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: 97 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