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Record W1970453800 · doi:10.1115/imece2006-15186

On Modeling and to Impact Dynamic Equations of Multi-Rigid-Link Robotics Having Simultaneous Collisions With Frictional Impulses

2006· article· en· W1970453800 on OpenAlexaff
Xiuping Mu, Qiong Wu, Yanping Mu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of ManitobaMcGill University
Fundersnot available
KeywordsRoboticsLink (geometry)Computer scienceMotion (physics)Robot manipulatorRobotControl engineeringArtificial intelligenceControl theory (sociology)SimulationEngineeringControl (management)

Abstract

fetched live from OpenAlex

Impact plays an important role in robotic manipulations. As robotic manipulators interact with their environment or objects, the motion of the system varies discontinuously and large impulsive forces are created at the surface of contact and are transmitted through the system, particularly when the motion of the manipulator is fast. With the demands for more precise and faster performance of robotic manipulators and minimizing potential damage of the system, being able to better simulate and control impact has become essential. In this paper, the impact dynamics describing simultaneous collisions of multi-link robotics with frictional impulses are investigated. The solutions for the after-impact velocities and impulses are provided in a closed form which can be easily employed for simulating robotic system contact states. The results are also important for the motion planning and impact control of robotic systems with contact 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.231
Teacher spread0.224 · 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 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
Published2006
Admission routes1
Has abstractyes

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