MétaCan
Menu
Back to cohort
Record W2030909078 · doi:10.1115/detc2003/dac-48845

The Robust Design of a Two-Wheeled Quasiholonomic Mobile Robot

2003· article· en· W2030909078 on OpenAlexafffund
Alessio Salerno, Jorge Angeles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPayload (computing)Mobile robotRobotControl theory (sociology)Computer scienceTrajectoryInertiaControl engineeringRobot kinematicsRobot controlEngineeringArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

The robust design of a novel mobile robot, which comprises two driving wheels and an intermediate body carrying the payload, is the subject of this paper. We prove that, by virtue of the robot architecture, the kinetostatic model of the system is isotropic. Moreover, regarding the robot dynamic response, a robust design problem is formulated by minimizing the design bandwidth of the generalized inertia matrix of the robot over its architecture parameters. Furthermore, design conditions are given for the robot performance in trajectory-tracking to be feasible. Finally, a numerical comparison of two design solutions, one feasible and one robust, is provided by means of simulation runs. We demonstrate that the robust design solution doubles robot performance in trajectory-tracking, while reducing the oscillations of the intermediate body, by 40%, when compared with the feasible solution.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.207
Teacher spread0.190 · 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

Citations1
Published2003
Admission routes2
Has abstractyes

Explore more

Same topicRobotic Mechanisms and DynamicsFrench-language works237,207