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Record W2915900135 · doi:10.1109/tmech.2019.2900001

A Motion Transmission Model for a Class of Tendon-Based Mechanisms With Application to Position Tracking of the da Vinci Instrument

2019· article· en· W2915900135 on OpenAlexafffund
Farshad Anooshahpour, Peyman Yadmellat, Ilia G. Polushin, Rajni V. Patel

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2019
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPulleyDisplacement (psychology)KinematicsPosition (finance)Control theory (sociology)Nonlinear systemHysteresisTrajectoryTendonTracking (education)Angular displacementSimulationTransmission (telecommunications)Motion (physics)EngineeringComputer sciencePhysicsAcousticsMechanical engineeringClassical mechanicsArtificial intelligenceAnatomyControl (management)

Abstract

fetched live from OpenAlex

Tendon-based motion/force transmission is a conventional approach in the design of surgical robots. However, due to compliance in the tendons and significant frictions between the tendons, the pulleys, and the sheath, tendon-based systems exhibit highly nonlinear behavior that, in particular, includes hysteresis. In this paper, based on the concepts of creep theory in belt drive mechanics, a novel motion transmission model is developed for tendon-pulley mechanisms. The developed model is of pseudokinematic type; specifically, it relates the output displacement to both the input displacement and the input force. The model parameters are identified for a da Vinci instrument, and the model performance is experimentally evaluated. The experimental results demonstrate greater than 50% improvement in terms of root-mean-square position error as compared to a more conventional friction/compliance-free kinematic model. The model is subsequently used for position control of the tip of the instrument, resulting in elimination of the hysteresis and in accurate trajectory tracking.

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 score0.893
Threshold uncertainty score0.562

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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