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Record W2391895653 · doi:10.1109/icvrv.2015.64

Advances in Physically-Based Modeling of Deformable Soil for Real-Time Operator Training Simulators

2015· article· en· W2391895653 on OpenAlexaff
Daniel Holz, Ali Azimi, Marek Teichmann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsCM Labs Simulations (Canada)
Fundersnot available
KeywordsTerrainComputer scienceSimulationVirtual realityReal-time simulationUnmanned ground vehicleOperator (biology)Training (meteorology)Aerospace engineeringRange (aeronautics)Virtual machineWork (physics)Human–computer interactionEngineeringArtificial intelligenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

In recent years, realistic simulation of vehicles on soft terrain has increasingly gained importance due to its use in operator training, mission planning and design. In this work we present a method which is tailored to the requirements of real-time simulation of soft soil in Virtual Reality applications. We combine models from terramechanics and soil mechanics in order to represent the interaction of vehicles and their digging tools with a deformable terrain, allowing us to simulate a broad range of vehicles, including earth moving equipment and planetary rovers on soft ground in real-time. We consider the impact of both wheel - ground and tool - ground interactions on the vehicle behavior by creating a fully coupled simulation of the dynamics of the vehicle and its environment. Furthermore we present verification experiments which show that our wheel - ground interaction method yields realistic results.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.393

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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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