UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU

dc.contributor.author Walter, Bryan
dc.contributor.author Oliver, James
dc.contributor.author Sannier, Adrian
dc.contributor.author Reiners, Dirk
dc.contributor.author Oliver, James
dc.contributor.department Mechanical Engineering
dc.date 2018-02-15T20:02:15.000
dc.date.accessioned 2020-06-30T06:03:19Z
dc.date.available 2020-06-30T06:03:19Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2006
dc.date.embargo 2015-02-09
dc.date.issued 2006-07-01
dc.description.abstract <p>Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention, and common swarm control methods require expensive computational power. This paper begins with a discussion on how to improve upon current user interfaces and then reviews a swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. Next, a more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the graphics processing unit (GPU) in today's graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU's pipeline [1]. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this paper presents a vision of how to combine these new interface ideas and performance enhancements into an effective swarm control interface and training simulator.</p>
dc.description.comments <p>This is a manuscript of an article from The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 3 (2006): 167, doi:<a href="http://dx.doi.org/10.1177/154851290600300304" target="_blank">10.1177/154851290600300304</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/me_pubs/134/
dc.identifier.articleid 1132
dc.identifier.contextkey 6627203
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath me_pubs/134
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/54983
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/me_pubs/134/2006_Oliver_SwarmControl.pdf|||Fri Jan 14 19:51:35 UTC 2022
dc.source.uri 10.1177/154851290600300304
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Graphics and Human Computer Interfaces
dc.subject.keywords Virtual Reality Applications Center
dc.subject.keywords Virtual Reality
dc.subject.keywords swarms
dc.subject.keywords UAV
dc.subject.keywords pheremones
dc.subject.keywords GPU
dc.title UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 5ccf5963-e33d-4d89-a5ce-f3d4fc78e115
relation.isOrgUnitOfPublication 6d38ab0f-8cc2-4ad3-90b1-67a60c5a6f59
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