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Record W2169525100 · doi:10.1017/s0263574709990233

A new approach to the dynamic parameter identification of robotic manipulators

2009· article· en· W2169525100 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRobotica · 2009
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInertial measurement unitKinematicsIdentification (biology)Computer scienceAccelerometerInertial frame of referenceComputationJoint (building)Units of measurementControl engineeringControl theory (sociology)Artificial intelligenceEngineeringAlgorithm

Abstract

fetched live from OpenAlex

SUMMARY This paper presents a novel systematic approach to identify the dynamic parameters of robotic manipulators. A sequential identification procedure is first proposed to deal with the difficulties usually encountered with standard approaches. An all-accelerometer inertial measurement unit (IMU) is also suggested to estimate the joint velocities and accelerations, which are traditionally obtained by differentiating the joint positions. The IMU kinematics and the computation method for estimation joint motion from IMUs are provided. The proposed method yields promising results in improving the identification precision compared to conventional methods. Finally, practical experiments are conducted to validate the theoretical results.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.458

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 head0.224
Teacher spread0.211 · 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