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Record W2041089162 · doi:10.1109/icas.2010.35

Modified Mecanum Wheels for Traversing Rough Terrains

2010· article· en· W2041089162 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTraverseTerrainRobotComputer scienceEngineeringAutomotive engineeringMechanical engineeringArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

Mecanum wheels give vehicles and robots autonomous omni-directional capabilities, while regular wheels don't. The omni-directionality that such wheels provide makes the vehicle extremely maneuverable, which could be very helpful in different indoor and outdoor applications. However, current Mecanum wheel designs can only operate on flat hard surfaces, and perform very poorly on rough terrains. This paper presents two modified Mecanum wheel designs targeted for complex rough terrains and discusses their advantages and disadvantages in comparison to regular Mecanum wheels. The wheels proposed here are particularly advantageous for overcoming obstacles up to 75% of the overall wheel diameter in lateral motion which significantly facilitates the lateral motion of vehicles on hard rough surfaces and soft soils such as sand which cannot be achieved using other types of wheels. The paper also presents control aspects that need to be considered when controlling autonomous vehicles/robots using the proposed wheels.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.359

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Quick stats

Citations30
Published2010
Admission routes1
Has abstractyes

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