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Estimating Rover Slope Climbing Ability from Single Wheel Experiments

2024· article· en· W4402904074 on OpenAlexafffund
Alexander Demishkevich, Krzysztof Skonieczny

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClimbingComputer scienceHill climbingGeologyArtificial intelligenceEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Single wheel tests are commonly done in the early design phase of small planetary exploration rovers to predict the rover’s overall mobility performance. This research explores the correlation between single wheel and full rover mobility experimentally. A key contribution is novel single wheel experimentation on inclined terrain. These experiments provide a conceptual bridge between single wheel tests on flat terrain and full rover tests on sloped terrain. Single wheel experimental data from sloped and flat terrain is used to estimate the slip ratio required for the rover to climb terrain inclined at a specific angle. The rover is predicted to climb a slope of 15 degrees with approximately 0.45 wheel slip ratio based on flat terrain data or with 0.6 wheel slip based on sloped terrain data. In fact, full rover experiments exhibit 0.8 slip, demonstrating that single wheel experiments underestimate wheel slip by approximately 0.35 (i.e. almost half of 0.8) in flat ground tests and by 0.2 (i.e. a quarter of the actual value) on sloped terrain. Due to such disparities, predictions of full rover slope climbing ability based on single wheel tests should include additional factors of safety.

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 score0.948
Threshold uncertainty score0.500

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 head0.017
GPT teacher head0.242
Teacher spread0.224 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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