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Record W2084668732 · doi:10.1109/icma.2005.1626522

Inverse Jacobian based hybrid impedance control of redundant manipulators

2006· article· en· W2084668732 on OpenAlexaff
Muhammad Faizan Shah, Rajni V. Patel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsJacobian matrix and determinantRedundancy (engineering)Control theory (sociology)Impedance controlInverseMotion controlComputer scienceTorqueInverse kinematicsSerial manipulatorPosition (finance)Moore–Penrose pseudoinverseMicromanipulatorRobotControl engineeringParallel manipulatorMathematicsEngineeringArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

This paper presents an efficient control scheme for compliant motion control of kinematically redundant manipulators and its evaluation using an experimental 7 degrees-of-freedom manipulator, REDIESTRO (a redundant dexterous isotropically enhanced, seven turning-pair robot). An inverse Jacobian based hybrid impedance control (IJ-HIC) scheme is proposed that provides a unified approach for combining compliant motion control, redundancy resolution, and user defined secondary tasks in a single methodology. The IJ-HIC uses the inverse of the manipulator Jacobian together with PD control and feed-forward computed torque to track desired force and position trajectories. The scheme is capable of controlling force and position of redundant manipulators and utilizes the manipulator redundancy by achieving user-defined additional tasks. The IJ-HIC is implemented for real-time control of REDIESTRO. Simulation and experimental results validate the IJ-HIC scheme, and demonstrate its capabilities for performing various tasks.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.007
GPT teacher head0.187
Teacher spread0.179 · 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 designTheoretical or conceptual
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

Citations14
Published2006
Admission routes1
Has abstractyes

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