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Record W2047383038 · doi:10.1115/1.4025743

Operational Space Formulation and Analysis for Rovers

2013· article· en· W2047383038 on OpenAlexaff
Martin Hirschkorn, József Kövecses

Bibliographic record

VenueJournal of Computational and Nonlinear Dynamics · 2013
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsRedundancy (engineering)Computer scienceContext (archaeology)Inertial frame of referenceControl engineeringControl theory (sociology)Generalized coordinatesSpace (punctuation)EngineeringControl (management)Artificial intelligenceMathematicsPhysics

Abstract

fetched live from OpenAlex

Many techniques have been developed for analyzing and evaluating mechanical systems for the purpose of improving design and control, such as the operational space formulation. It has been been shown to be a useful tool when working with robotic manipulators, but has not been extended to consider rovers. Rovers are fundamentally different due to the wheel-ground contact, that does not exist for fixed-base systems. In this paper, several different aspects of the operations space formulation, inertial properties, control, multi-arm systems, redundancy, and unactuated coordinates are investigated in the context of rovers. By considering a different interpretation of the operational space of a rover, several sets of generalized coordinates were chosen to represent the movement of two example rovers. Simulations were performed to demonstrate how these choices of generalized coordinates can be used to analyze various characteristics of the rovers and can improve the behavior for certain maneuvers, such as wheel walking.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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