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Record W2065324905 · doi:10.1109/i2mtc.2012.6229676

Biomimetic measurement of optical flow and centroid for visual-servo control of hover flight

2012· article· en· W2065324905 on OpenAlexaff
Philip Crnko, David W. Capson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCentroidControl theory (sociology)FovealComputer scienceServoThrustOptical flowServomotorPID controllerComputer visionEstimatorController (irrigation)Servo controlVisual servoingControl engineeringArtificial intelligenceEngineeringRobotControl (management)MathematicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents a novel, biomimetic design of a visual sensor and visual servo system for a micro-RUAV, based on a study of the hybrid controls approach used by Macroglossum Stel. L. for pure 2-D hover stabilization. The sensor approach determines ego-motion in the Yaw axis through use of an optic flow estimator on a peripheral ring about a foveal region centered on a target. A centroid measure of the target is used to govern remaining degrees of freedom. The biomimetic controller builds upon locomotor studies and uses a PID control to govern error corrections for each separate axis. In turn, the corrective motions of each axis are combined through a thrust matrix, which projects the desired thrust on a series of pre-learned signals in cardinal directions about the RUAV. Experimental implementation and results are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations3
Published2012
Admission routes1
Has abstractyes

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