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Record W2577103746 · doi:10.1115/1.4035683

Design and Static Analysis of Elastic Force and Torque Limiting Devices for Safe Physical Human–Robot Interaction

2017· article· en· W2577103746 on OpenAlex
Meiying Zhang, Thierry Laliberté, Clément Gosselin

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 · 2017
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTorqueStiffnessSpring (device)Mechanism (biology)Control theory (sociology)Nonlinear systemLimiterRestoring forceTorque limiterEngineeringComputer scienceMechanical engineeringStructural engineeringPhysicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the static analysis of elastic force and torque limiters that aim at limiting the forces that a robotic manipulator can apply on its environment. First, the design of one-degree-of-freedom force and torque limiting mechanisms is presented. It is shown that a single elastic component (spring) can be used to provide a prescribed preload and stiffness in both directions of motion along a given axis. Then, the mechanisms are analyzed in order to determine the nonlinear relationships between the motion of the mechanism and the extension of the spring. These relationships can then be used in the design of the force and torque limiters. Finally, the force capabilities of the mechanisms are investigated and numerical results are provided for example designs.

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: none
Teacher disagreement score0.640
Threshold uncertainty score0.335

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 head0.023
GPT teacher head0.284
Teacher spread0.260 · 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