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Record W2079846333 · doi:10.1115/detc2008-49630

Design and Control of an Omnibot Autonomous Vehicle

2008· article· en· W2079846333 on OpenAlexafffund
Steven Bemis, Brian Riess, Scott Nokleby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsOntario Tech University
FundersUniversity of Ontario Institute of Technology
KeywordsObstacleController (irrigation)Obstacle avoidanceComputer scienceMobile robotCollision avoidancePath (computing)Control systemCollisionRouting (electronic design automation)Control engineeringRobotReal-time computingEngineeringEmbedded systemArtificial intelligence

Abstract

fetched live from OpenAlex

A design for autonomous control of a novel omni-directional platform is presented. This platform is to be used in conjunction with a robotic arm to further research of mobile-manipulator systems. This design differs from other omni-directional platforms that use omniwheels in that its drive axes do not intersect its geometric centre. The platform can be controlled autonomously through multiple sub-systems that have been designed including a closed-loop velocity controller, a localization system, an obstacle avoidance and collision detection system, a vision system, and a data routing system. These sub-systems communicate with a remote computer which plots the path and sends data to guide the platform. The closed-loop velocity controller provides feedback which can be used to analyze and correct the path of travel.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.236
Teacher spread0.208 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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