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Record W2085381166 · doi:10.1115/detc2010-28280

Fingertip Force/Torque Sensor With High Isotropy and Sensitivity for Underwater Manipulation

2010· article· en· W2085381166 on OpenAlexaff
Qiaokang Liang, Dan Zhang, Zhongzhe Chi, Yunjian Ge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsUnderwaterTorqueSensitivity (control systems)RobotControl theory (sociology)LinearityIsotropyComputer scienceControl engineeringEngineeringControl (management)PhysicsArtificial intelligenceElectronic engineeringGeology

Abstract

fetched live from OpenAlex

Control strategies for robotic manipulators in underwater applications are still immature compared with the strategies of the manipulators on land. Part of the reason is that there is no precise force/torque information, which is essential to the close-loop control. Unlike the sensor applied on the ground, the sensors for underwater applications have to endure the high-pressure, low-temperature and corrosive environment. Therefore, aimed at obtaining the accurate interaction force/torque between underwater robot manipulators and objects, a novel four-dimensional fingertip force sensor is presented based on e-type membrane for underwater robot manipulators. A seal technique is described. Experimental results demonstrate the design could detect force/torque with good linearity, high sensitivity and weak couplings.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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 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

Citations3
Published2010
Admission routes1
Has abstractyes

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