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Record W2142695785 · doi:10.1177/0278364909101231

Haptic Effects of Surgical Teleoperator Flexibility

2009· article· en· W2142695785 on OpenAlexaff
Mahdi Tavakoli, Robert D. Howe

Bibliographic record

VenueThe International Journal of Robotics Research · 2009
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Alberta
FundersUniversity of PennsylvaniaNational Science Foundation
KeywordsTeleoperationHaptic technologyFlexibility (engineering)RobotSimulationTransparency (behavior)Contact forceComputer scienceTeleroboticsEngineeringControl theory (sociology)Artificial intelligenceControl (management)Mobile robotPhysics

Abstract

fetched live from OpenAlex

Minimally invasive surgery systems typically involve thin and cable-driven surgical instruments. This introduces link and joint flexibility in the slave robot of a master—slave teleoperation system, reducing the effective stiffness of the slave and the transparency of teleoperation. In this paper, we analyze transparency under slave link and joint flexibility (tool flexibility). We also evaluate the added benefits of using extra sensors at the tip of the flexible robot. It is shown that tip velocity (or position) feedback improves free-space position tracking performance in the presence of robot flexibility. Also, when the interaction forces with an environment are measured by a force sensor and fed back to the user’s hand, tip velocity feedback improves hard-contact force tracking performance. During a hard contact task, tip velocity feedback can also eliminate the transmission of robot flexibility to the user’s hand.

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)0.000
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.358
Teacher spread0.313 · 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

Citations51
Published2009
Admission routes1
Has abstractyes

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