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Record W2007481439 · doi:10.1109/icuas.2014.6842279

Long-range communication framework for multi-agent autonomous UAVs

2014· article· en· W2007481439 on OpenAlexafffundabout
Mammadov Elchin, Wail Gueaieb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsPayload (computing)Network packetComputer scienceWireless ad hoc networkReal-time computingTelemetryWirelessRadio controlCommunications systemComputer networkTelecommunications

Abstract

fetched live from OpenAlex

Multiple unmanned aerial vehicles (UAVs) with inter-UAV communication capabilities can be used to extend the communication range with the ground control station (GCS). Researchers from the Mechanical and Electrical Engineering at the University of Ottawa have developed a new dirigible autonomous UAV with a flight duration of 24+ hrs, a limitied payload of 1 kg for electronics, and requiring a communication range of 1-10 kilometres. To support this requirement a new communication framework was introduced and implemented based on the ad hoc network concepts. With one radio module per dirigible the designed and developed wireless interface allows any UAV or the GCS to exchange flight control commands, telemetry data, and aerial photos. We made use of the advanced networking tools of the Digi's 9XTend™ radio modules to develop route tracing, traffic prioritization, and minimizing self-interference between simultaneous transmissions. Initial test results showed that without any data flow control in the network, packets can be received in the wrong order following different routes and cause errors in the transmission of photos or recorded video. This issue was resolved through acknowledgements to control the flow of packets. Using radios with half-wavelength dipole antennas we were able to achieve a one-hop range of up to 5 km with the radio-frequency line-of-sight.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.492
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.262
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2014
Admission routes3
Has abstractyes

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