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Record W3200854823 · doi:10.1017/s0263574721001223

Design and development of a novel autonomous scaled multiwheeled vehicle

2021· article· en· W3200854823 on OpenAlexaff
Aaron Hao Tan, Michael Peiris, Moustafa El–Gindy, Haoxiang Lang

Bibliographic record

VenueRobotica · 2021
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPlannerMotion planningComputer sciencePath (computing)Work (physics)Visual servoingControl engineeringSteering wheelAutomotive engineeringSimulationArtificial intelligenceEngineeringRobot

Abstract

fetched live from OpenAlex

Abstract This article proposes the design and development of a novel custom-built, autonomous scaled multiwheeled vehicle that features an eight-wheel drive and eight-wheel steer system. In addition to the mechanical and electrical design, high-level path planning and low-level vehicle control algorithms are developed and implemented including a two-stage autonomous parking algorithm is developed. A modified position-based visual servoing algorithm is proposed and developed to achieve precise pose correction. The results show significant gains in accuracy and efficiency comparing with an open-source path planner. It is the aim of this work to expand the research of autonomous platforms taking the form of commercial and off-road vehicles using actuated steering and other mechanisms attributed to passenger vehicles. The outcome of this work is a unique autonomous research platform that features independently driven wheels, steering, autonomous navigation, and parking.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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 designBench or experimental
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

Citations6
Published2021
Admission routes1
Has abstractyes

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