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Record W1993008219 · doi:10.1109/haptics.2008.4479970

Analysis and Experimentation of a 4-DOF Haptic Device

2008· article· en· W1993008219 on OpenAlexaff
Athen Ma, Shahram Payandeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsKinematicsHaptic technologyStylusComputer scienceBall (mathematics)Inverse kinematicsWorkspaceOrientation (vector space)Universal jointSimulationMechanical engineeringGeometryEngineeringComputer visionArtificial intelligenceRobotPhysicsMathematicsClassical mechanics

Abstract

fetched live from OpenAlex

This paper presents a new configuration of a haptic device based on a 4-DOF hybrid spherical geometry. The spherical parallel ball support (SPBS) type device, as it is referred to, consists of a particular design with the intersecting axes of both active and passive spherical joints. The orientation of the device is determined by the mobile platform on the active spherical joint using a special class of spherical 3-DOF parallel geometry. The passive spherical joint (ball and socket configuration) is used to increase the mechanical fidelity of the device. Previously it has been shown that some configurations of this spherical parallel geometry lead to kinematic optimization. We introduce the design consisting of a stylus such as a laparoscopic gripper attached on the mobile platform supporting an additional translational motion. First, the new forward and inverse kinematics analysis is presented. Our work is motivated by deriving a closed-form solution of the kinematics for this proposed device configuration. Then, a closed-loop system framework which can be used for real-time force feedback control is outlined. Finally, preliminary experimental results obtained with the prototype are presented.

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 score0.505
Threshold uncertainty score0.117

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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