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Record W7095299002

AIAA-2000-4087 ADVANCED SPACE ROBOTICS SIMULATION FOR TRAINING AND OPERATIONS

2008· article· en· W7095299002 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPayload (computing)International Space StationRoboticsTask (project management)Space (punctuation)RobotMission control centerTraining (meteorology)Flight simulator
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the advances being made in space robotics simulation to meet the challenges of astronaut training and space operations support. This simulator (MOTS) is being used to train International Space Station astronauts to perform on-orbit robotics tasks. It also supports mission planning and task verification in an operationally representative environment. The simulator supports critical tasks to be performed by astronauts including payload handling, berthing and de-berthing. MOTS is a state-of-the-art simulator providing astronauts with a simulation representative of the space station dynamics and visual environment. It provides real-time high-fidelity simulation of the flexible dynamics performance of two robotic arms (space station arm and shuttle arm) concurrently in a micro-gravity environment to support complex "hand-off " tasks. Contact dynamics models have been added to enhance the realism of berthing payloads to the Space Station with multiple contact points simultaneously tracked. 3D visual models support realistic views generated by the space station cameras in an operational and dynamic lighting environment that includes the production of split screen views. The incorporation of the Space Station Robot Arm Flight Control System Software provides an invaluable and confident environment in which on-orbit tasks is being planned and practiced. MOTS is also being integrated into several facilities at the Canadian Space Agency such as the Space Operations and Support Centre, to support on-line diagnostics.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.469
Threshold uncertainty score0.339

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 head0.058
GPT teacher head0.282
Teacher spread0.224 · 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
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
Published2008
Admission routes1
Has abstractyes

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