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Record W2096312019 · doi:10.1109/hipc.1996.565806

Distributed computation of inverse dynamics of robots

2002· article· en· W2096312019 on OpenAlexaff
R. Rajagopalan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceInverse dynamicsComputationScheduling (production processes)RobotParallel computingTask (project management)Mathematical optimizationAlgorithmArtificial intelligenceMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a task scheduling to perform parallel computation of dynamics of robots. For illustration, the inverse dynamic analysis considered is based on the Newton-Euler recursive formulation. The results are presented for a robot with six degrees of freedom. The task scheduling has been prepared using the time taken to execute mathematical operations, transfer data between processors and perform substitutions for array operations. The paper reports a comparative study between T800 transputers and TMS320C40 parallel processors. It can be seen that the use of a single C40 processor results in 81% and faster performance compared to a single T800 while the use of two and three transputers provide a speed up of 28.8% and 43% respectively. Implementation of the task scheduling has been performed employing INMOS T800-20 transputers to evaluate the dynamics of Puma 560 robot without simplifying the general form of the equations employed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.189
Teacher spread0.176 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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