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Record W2793838380 · doi:10.1109/icamechs.2017.8316569

Impact dynamics in robotic and mechatronic systems

2017· article· en· W2793838380 on OpenAlexaff
Farhad Aghili, Chun‐Yi Su

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsJacobian matrix and determinantControl theory (sociology)AccelerationTopology (electrical circuits)Projection (relational algebra)Impulse (physics)RobotBounded functionOblique projectionCoefficient of restitutionMathematicsComputer scienceApplied mathematicsMathematical analysisPhysicsClassical mechanicsAlgorithmGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents an impact model of robotic manipulators subject to changing topology using the notion of linear projection operators. An oblique projection matrix is introduced in order to directly obtain: i) The acceleration of the constrained robotic system from unconstrained acceleration, ii) post-impact velocity of the robot from pre-impact velocity, and iii) impulse force during impact from pre-impact momentum. All solutions are provided in closed-form with elegant geometrical interpretations, e.g., the energy lost during contact is a quadratic function of pre-impact velocities. Moreover, the formulation seemingly works not only when the unilateral constraints remain active through a finite time interval but when changing topology occurs, i.e., unilateral constraints becomes inactive or vice versa, or even when the overall constraint jacobian becomes singular. The formulation is general enough to be applicable to robotic manipulators with chain, tree and close-loop topologies, or to handle simultaneous multiple contacts with non-identical restitution coefficients. The model is proven to be energetically consistent if a global restitution coefficient bounded between zero and one is assumed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.332

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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