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Record W2300650771 · doi:10.1115/detc2015-46626

Underactuated Finger Closing Motion Control Using Dual Drive Actuation

2015· article· en· W2300650771 on OpenAlexaff
Jean-Michel Boucher, Lionel Birglen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsUnderactuationKinematicsActuatorClosing (real estate)Control theory (sociology)Computer scienceMechanism (biology)Motion (physics)Motion controlWork (physics)Control engineeringEngineeringArtificial intelligenceControl (management)RobotMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a novel technique to prescribe and control the closing motion of a linkage-driven underactuated finger is presented. Since an underactuated, a.k.a self-adaptive, finger generally only has one actuator for a given number of degrees of freedom, its closing motion before making contact with an object is typically imposed by its mechanical design and cannot be changed once the finger is built. In the literature, several closing motions for underactuated fingers have been proposed each one having its own merits and in each case, associated to a particular mechanical layout. In this work, the authors propose a novel design of a partially compliant underactuated finger based on a dual drive actuation system where two motors, which can be used independently or in combination, move the finger. Each of these motors prescribes a different closing motion which has been selected amongst the most commonly found in the literature. In order to characterize the behavior and performances of this finger, a kinetostatic analysis is carried on and a lumped compliance model is developed. The geometry of the finger is then optimized using a genetic algorithm in order to achieve the desired kinematic motions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.390

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 head0.066
GPT teacher head0.264
Teacher spread0.199 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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