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Record W2119968544 · doi:10.1109/aim.2011.6027015

Development of a compact wrist with multiple working modes

2011· article· en· W2119968544 on OpenAlexaff
Hongwei Zhang, Yugang Liu, Guangjun Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceKinematicsTorqueCompensation (psychology)Controller (irrigation)SimulationMode (computer interface)Position (finance)Inverse kinematicsControl engineeringWristTracking (education)Control theory (sociology)EngineeringControl (management)Artificial intelligenceRobot

Abstract

fetched live from OpenAlex

This paper presents the design, modeling and control of a compact wrist, which can work in active mode with position or torque control, or passive mode with interactive force compensation. This characteristic makes it suitable for dexterous manipulation in unstructured environments, such as door opening. Mechanism design is presented briefly, and forward, inverse as well as differential kinematics are derived to map the motions and velocities between the task space and joint space. A distributed robust adaptive controller is developed for tracking control of the wrist in active mode; and a new interactive force compensation technique is proposed on the basis of force sensor measurement, which enables passive working mode of the compact wrist. A DSP based control system is developed to test the developed control methods. A prototype has been fabricated and some preliminary experiments have been conducted to validate the proposed design and to verify the developed algorithms.

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)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.203
Teacher spread0.144 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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