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Record W2938668785 · doi:10.3390/robotics8020033

Impedance Control Self-Calibration of a Collaborative Robot Using Kinematic Coupling

2019· article· en· W2938668785 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRobotics · 2019
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsLaser trackerCalibrationKinematicsRobotImpedance controlElectrical impedanceCoupling (piping)Robot calibrationLaserFiducial markerComputer scienceEngineeringSimulationControl theory (sociology)Robot kinematicsArtificial intelligenceControl (management)Mobile robotMechanical engineeringElectrical engineeringOpticsMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper presents a closed-loop calibration approach using impedance control. The process is managed by a data communication architecture based on open-source tools and designed for adaptability. The calibration procedure uses precision spheres and a kinematic coupling standard machine tool components, which are suitable for harsh industrial environments. As such, the required equipment is low cost (approximately $2000 USD), robust, and is quick to set up, especially when compared to traditional calibration devices. As demonstrated through an experimental study and validated with a laser tracker, the absolute accuracy of the KUKA LBR iiwa robot was improved to a maximum error of 0.990 mm, representing a 58.4% improvement when compared to the nominal model. Further testing showed that a traditional calibration using a laser tracker only improved the maximum error by 58 µm over the impedance control approach.

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.

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 score0.830
Threshold uncertainty score0.533

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 head0.011
GPT teacher head0.227
Teacher spread0.217 · 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