hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID iD: 0000-0002-0350-4621
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID iD: 0000-0002-2803-9607
Brazilian Army, Brasilia, Brazil.
Show others and affiliations
2018 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 72, p. 10-20Article in journal (Refereed) Published
Abstract [en]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2018. Vol. 72, p. 10-20
Keywords [en]
Unmanned Aerial Vehicles, Task allocation, Multi-agent systems, Swarm intelligence
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-38317DOI: 10.1016/j.engappai.2018.03.008ISI: 000434239000002Scopus ID: 2-s2.0-85044458179OAI: oai:DiVA.org:hh-38317DiVA, id: diva2:1263189
Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-11-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Zacarias, IulisloiPignaton de Freitas, Edison

Search in DiVA

By author/editor
Zacarias, IulisloiBazzan, Ana L.C.Pignaton de Freitas, Edison
In the same journal
Engineering applications of artificial intelligence
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 28 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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