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Record W2038573003 · doi:10.1115/esda2012-82782

Towards Delayed Teleoperation With Pneumatic Master and Slave for MRI

2012· preprint· en· W2038573003 on OpenAlexaff
Arnaud Lelevé, Minh Tu Pham, Mahdi Tavakoli, Richard Moreau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTeleoperationActuatorRobotHaptic technologyTeleroboticsMaster/slaveSimulationPneumatic actuatorComputer scienceControl engineeringControl theory (sociology)EngineeringArtificial intelligenceMobile robotControl (management)

Abstract

fetched live from OpenAlex

Over the last 50 years, master-slave teleoperation has become a widespread and successful field of research. This discipline explores how to perform tasks using a robot on an environment with haptic feedback about robot-environment interaction being provided to the human operator. Most of the master and slave manipulators used in teleoperation are electrically actuated. However, in some particular applications such as inside an MRI for image-guided surgery, ferromagnetic materials including electrical wiring is prohibited. Thus, non-ferromagnetic actuators like pneumatic or hydraulic actuators are a solution to this problem. This specific application also requires teleoperation in the sense of “tele-actuation” because of the lack of space inside the MRI chamber to put the robot’s actuators and the presence of electrical components in pneumatic servovalves. In this paper, we study the case of a teleoperation system composed of two identical pneumatic cylinders (as the master and the slave) equipped with servovalves, making a symmetric teleoperation system. This serves as a one-degree-of-freedom system to outline the design and analysis in terms of teleoperation transparency and stability. Simulation and experimental results check the validity of the theory without and with classical transmission delays.

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 score0.878
Threshold uncertainty score0.712

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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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