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Record W3005386404 · doi:10.1109/lra.2020.2970944

6-DOF Force Sensing for the Master Tool Manipulator of the da Vinci Surgical System

2020· article· en· W3005386404 on OpenAlexafffund
David Black, Amir Hossein Hadi Hosseinabadi, Septimiu E. Salcudean

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJoystickSurgical robotHaptic technologySoftwareManipulator (device)Interface (matter)TorqueImpedance controlSimulationEngineeringRobotComputer scienceEmbedded systemArtificial intelligenceOperating systemPhysics

Abstract

fetched live from OpenAlex

We integrated a force/torque sensor into the wrist of the Master Tool Manipulator (MTM) of the da Vinci Standard Surgical system. The added sensor can be used to monitor the surgeon interaction forces and to improve the haptic experience. The proposed mechanical design is expected to have little effect on the surgeon's operative experience and is simple and inexpensive to implement. We also developed a software package that allows for seamless integration of the force sensor into the da Vinci Research Kit (dVRK) and the Robot Operating System (ROS). The complete mechanical and electrical modifications, as well as the software packages are discussed. Two example applications of impedance control at the MTM and joystick control of the PSM are presented to demonstrate the successful integration of the sensor into the MTM and the interface to the dVRK.

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)0.001
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.203
Teacher spread0.177 · 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

Citations36
Published2020
Admission routes2
Has abstractyes

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