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Record W2122536841 · doi:10.1017/s0263574704000645

Dynamic modeling of tip-over stability of mobile manipulators considering the friction effects

2005· article· en· W2122536841 on OpenAlexaff
R. F. Abo-Shanab, Nariman Sepehri

Bibliographic record

VenueRobotica · 2005
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsExcavatorBase (topology)LoaderControl theory (sociology)Stability (learning theory)EngineeringSuspension (topology)Computer scienceStructural engineeringMechanical engineeringControl (management)MathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper extends the models developed previously by the authors for simulating tip-over stability of mobile manipulators, to include the friction of the contact between the base and the ground. Thus, the present model takes into account the detailed dynamics of the base that can rock back and forth during the movement of the manipulator, the combined vehicle suspension and ground-tire compliance and, the friction between the wheels and the ground. ‘LuGre’ tire friction model is employed, which along with the novel method of virtual links transforms the system into a fixed base manipulator with single degree of freedom at each joint. The model is then used to simulate planar movements of a 215B Caterpillar excavator-based log-loader machine. The results are also compared to those obtained by the simplified model, which was developed previously based on the assumption that the friction between the base and the ground is high enough to prevent the base from skidding forward or backward. The results clearly show that the friction properties between the wheels and the ground affect machine stability. Thus, one has to include the frictional effect in order to accurately predict the tip-over behavior of mobile manipulators.

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: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.333

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 head0.010
GPT teacher head0.215
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

Citations12
Published2005
Admission routes1
Has abstractyes

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