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Record W2014113893 · doi:10.1088/0960-1317/17/5/024

Development of a long-range untethered frictional microcrawler

2007· article· en· W2014113893 on OpenAlexafffund
Matthew Brown, Ted Hubbard, Marek Kujath

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsStictionClimbCrawlingActuatorRange (aeronautics)RobotMechanicsStatic frictionVoltageMechanical engineeringEngineeringSimulationElectrical engineeringMaterials sciencePhysicsComputer scienceAerospace engineeringNanotechnologyMicroelectromechanical systemsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper examines the development and performance of a new type of microcrawler. The frictional microcrawler is distinguished from previous microcrawling robots in that it operates by taking advantage of the friction/stiction present between its segments and the surface upon which it travels. It is driven by conventional thermal actuators that create a sequence of horizontal forces that push the microcrawler forward against friction forces one segment at a time. It can be precisely positioned by accumulating micron sized steps. It is capable of long-range reversible motion which is limited only by the length of the tracks along which it travels. The microcrawler requires less than 3 V to operate and it can travel at velocities exceeding 700 µm s−1. It can develop a horizontal force greater than 130 µN and it continues to operate reliably while carrying a load of over 100 times its own weight of 1 µN. Experiments have shown that it can even climb a vertical wall. The effect of input frequency, actuation voltage and size of contact area on the measured crawling velocity and force are presented and 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.596

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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 designBench or experimental
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

Citations10
Published2007
Admission routes2
Has abstractyes

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