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Record W2161006934 · doi:10.1109/robot.2000.844822

Motion/force/image control of a diagnostic ultrasound robot

2002· article· en· W2161006934 on OpenAlexaff
Wen-Hong Zhu, Septimiu E. Salcudean, S. Bachmann, Purang Abolmaesumi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsController (irrigation)SonographerRobotMotion controlFeature (linguistics)Computer scienceTorqueTrajectoryControl theory (sociology)ServomotorPosition (finance)Control systemRobot end effectorDC motorRobot controlArtificial intelligenceComputer visionEngineeringControl (management)UltrasoundMobile robotAcousticsPhysics

Abstract

fetched live from OpenAlex

The authors previously (1999) presented a fully counter-balanced 6-DOF robot for ultrasound carotid artery diagnosis. This paper presents the design of its controller. The controller is a velocity controller capable of incorporating position control, force control, and image based control, using shared control. Safety is a significant issue in the design of the control system. The counterbalanced mechanical design enables the use of small motor torques which result in 10N maximum force at the probe, and allows the probe to remain in its position in case of power failure. The robot can be positioned by an input device via motion/force control and by image feature based control. It can also be positioned by directly pushing its linkage in case that either the patient or the sonographer shows the slightest sign of discomfort. Feasibility experiments are reported.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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

Citations61
Published2002
Admission routes1
Has abstractyes

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