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Record W2974276093 · doi:10.1109/coase.2019.8843061

Control of a mechatronics-assisted system for surgeries using flexible tools

2019· article· en· W2974276093 on OpenAlexaff
Lingbo Cheng, Mahdi Tavakoli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMechatronicsTeleoperationFlexibility (engineering)Impedance controlFeed forwardControl engineeringRobotTransparency (behavior)Control systemEngineeringElectrical impedanceRoboticsSimulationComputer scienceControl theory (sociology)Control (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Flexible and lightweight surgical tools have the potential to significantly increase the dexterity of mechatronics-assisted surgical systems for minimally invasive surgeries. However, the control of a mechatronics-assisted system with the link and joint flexibility is quite challenging and needs to be studied. In this paper, a bilateral impedance-controlled master-slave teleoperation system is considered, where the slave (surgical) robot is flexible. Two reference impedance models are designed for the master and slave robots to control the mechatronics-assisted system. Also, depending on different feedback and feedforward signals, four cases are distinguished. To obtain better transparency of the system, the tuning rules for the impedance parameters for each case are presented and the corresponding transparency measures are analyzed and compared. As a result, by appropriately adjusting the impedance model parameters, ideal position and force tracking can be attained for a teleoperation system with a flexible surgical robot. The theoretical findings are validated in simulations.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.289

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 head0.029
GPT teacher head0.229
Teacher spread0.200 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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