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Record W2036766588 · doi:10.2514/6.2010-8696

VR Simulation System for EVA Astronaut Training

2010· article· en· W2036766588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Exploration and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceTraining (meteorology)SimulationTraining systemHuman–computer interactionAeronauticsEngineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

The objective of this research is to develop a VR simulation system for EVA astronaut training. The key techniques relating to VR training system are studied, which include astronaut body motion tracking, hand motion tracking, hand force feedback, and space scene construction. The human-computer interaction with both visual and force feedback are carried out in the system. The force feedback of hand operating enhances the astronaut realistic feeling in training. A case study on training astronauts for space walking and load retrieve has been conducted and the experimental results demonstrate effectiveness and usability of the system. I. Introduction owadays, the manned spaceflight project is constantly improved that astronauts will be confronted with more and more spaceflight mission. EVA (Extravehicular Activity) is one of the basic technologies for manned spaceflight mission. In EVA astronauts will be faced with various space operations so that they need to get adequate training on the ground to master perfect manipulative skills of working in space. Astronaut training methods therefore play an important role for space mission preparation and should be extended and improved. N Virtual reality technology as a training method has advantages of digitization, reusability, safety and being able to go through the limitations of physical environments that it has already become an effective means for astronaut training on the ground. Since 1980’s, the researches of virtual reality techniques used in astronaut training have been conducted in NASA, ESA and Canada and accomplished outstanding achievements. In previous researches the human-computer interaction mainly focused on visual feedback rather than force feedback. In this paper we developed a VR Simulation System in which both visual and force feedback is provided to astronaut to make training more realistic. The paper firstly describes the framework of the VR simulation system for EVA astronaut training. Then give the implementation in detail of body motion tracking, hand motion tracking, hand force feedback, and construction of virtual space scene. A case study on training astronauts for space walking and load retrieve is conducted at last to validate the system performance and usability. The experimental results demonstrate that the VR simulation system given in this research can be directly used to train astronaut for EVA preparation.

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.

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: none
Teacher disagreement score0.866
Threshold uncertainty score0.177

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 head0.244
Teacher spread0.221 · 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

Quick stats

Citations11
Published2010
Admission routes1
Has abstractyes

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