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Autonomous Robot for Mining Exploration: A Structomatical and Kinematical Model for Uneven Ground

2015· article· en· W2247531297 on OpenAlexaff
M. Éné, Ion Simionescu, Victor Moise, Iulian Tabără, Maxime Mailloux

Bibliographic record

VenueApplied Mechanics and Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsChassisMechanism (biology)HolonomicKinematicsEngineeringRobotControl engineeringComputer scienceMechanical engineeringPhysicsArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

This paper deals with the analysis of the mechanical system of a self-propelled vehicle on the tires able to move on an uneven ground whilst his platform stays horizontally. It is question to simulate the movement of a desmodromic robot which moves in an environment represented by a 3D surface. The robot has a mechano-hydraulic system which is able to modify the geometry of chassis in the aim of maintaining the platform always at horizontal while in movement, no matter the soil configuration (of course between some limits).The horizontalisation mechanism with the rolling train hydraulically driven presents some difficulties because of the non holonomic constraints of the wheels ([1, 2]). In order to make the application of the multipoles theory in the structomatical model must be introduce some simplifications in the contact joint. Thus, the non holonomic joints are presented like gamma active joints (with the condition of controlled rolling/skidding).This is an extension of the general principle of mechanism formation ([3]) according who any mechanical structure can be broken in genes upon an unique formula (the genetic code of the mechanism).Because of the complexity of the calculus the study of mechanism was divided in a few chapters: geometrics, structomatics, kinematics, kinetostatics and dynamics etc. The uttermost important and difficult part is the kinematical model because of the non-linearity of the equations. This article presents the first two items; the others will be the matter of future papers.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.553

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 head0.035
GPT teacher head0.236
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

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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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