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Record W2003069630 · doi:10.1109/robot.2010.5509283

Characterization of the electrical resistance of carbon-black-filled silicone: Application to a flexible and stretchable robot skin

2010· article· en· W2003069630 on OpenAlexaff
Marc-Antoine Lacasse, Vincent Duchaine, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSiliconeRobotElectrical conductorComputer scienceFabricationMaterials scienceCarbon blackElectrical resistivity and conductivityCharacterization (materials science)Function (biology)Mechanical engineeringComposite materialNanotechnologyArtificial intelligenceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Providing robots with the capability of sensing their surrounding environment is an important feature that would lead to a more intuitive and safe physical human-robot interaction. This paper proposes a new design of homogeneous flexible and stretchable robot skin based on carbon-black-filled (CBF) silicone and conductive fabric that can sense multiple contact locations as well as applied pressure. CBF silicone has been already used in sensing technology but its piezoresistivity is still largely misunderstood. This particular behavior is investigated in this paper through a set of experiments conducted on isolated sensing cells. Using the results of these experiments, a model describing the variation of the resistivity in the CBF silicone as a function of the applied pressure is proposed. Based on this model, a simple way to accurately estimate the applied pressure in real time is demonstrated. Finally, using this improved knowledge of the behaviour of the CBF silicone, the fabrication of a fully functional sensor array is presented. The proposed design has the particularity of circumventing the well-known problem of cross-talk between sensing cells.

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 score0.018
Threshold uncertainty score0.229

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations66
Published2010
Admission routes1
Has abstractyes

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