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Record W3108572984 · doi:10.1115/1.4049191

Mechanical Design of a Low-Impedance 6-Degree-of-Freedom Displacement Sensor for Intuitive Physical Human–Robot Interaction

2020· article· en· W3108572984 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2020
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinematicsMechanical impedanceDisplacement (psychology)RobotMechanism (biology)Electrical impedanceInverse kinematicsComputer scienceMechanical systemEngineeringRobot kinematicsControl theory (sociology)SimulationControl engineeringMechanical engineeringMobile robotArtificial intelligencePhysicsElectrical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

Abstract This paper presents the mechanical design of a six-degree-of-freedom low-impedance displacement sensor. The sensor is mounted around a link of a serial robot and used as an interface for physical human–robot interaction. The motivation for the use of a low-impedance sensor is first discussed. The mechanical design of each of the elastic components of the sensor is then presented. The kinematic architecture of the mechanism is introduced, and the inverse and forward kinematic problems are solved. The kinematic sensitivity is then used to characterize the accuracy of the mechanism. Finally, the design of a prototype is presented and experimental results are provided.

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.

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 score0.315
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.267
Teacher spread0.226 · 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