MétaCan
Menu
Back to cohort
Record W2886417522 · doi:10.1061/9780784479971.033

Towards Mobile 3D Printing for Planetary Construction

2016· article· en· W2886417522 on OpenAlexaff
Shawn Bulger, Krzysztof Skonieczny

Bibliographic record

VenueEarth and Space 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceAstrobiologyPhysics

Abstract

fetched live from OpenAlex

3D printing presents a new and exceptionally versatile class of construction tasks for robots to undertake using granular materials found in planetary terrains. For space applications, a printing head on a mobile robotic platform could free 3D printing from the constraints of a printer’s workspace, enabling arbitrarily large structures and objects to be built with small robots. Mobile 3D printing consists of building compound parts by looping over 3 high-level steps: (1) printing directly onto the ground surface (and directly onto pre-existing segments as well); (2) relocating from one static build location to the next; (3) accurately and precisely establishing the location of the relevant interface on a pre-existing segment of the printed object, relative to the current position of the robot after relocation. This work presents an approach to mobile 3D printing that addresses these required steps in significantly greater detail than the current literature. A Rostock Delta 3D printer configuration with fused filament extrusion printing is selected to enable direct printing onto the ground surface. Printing directly onto pre-existing segments is demonstrated with a series of thermoplastic parts. Preliminary experimental stress analysis does not reveal any obvious weaknesses at the seam of a compound part, when compared to a monolithic part. Localization techniques are proposed that utilize structured light surface sensing combined with laser range-finder triangulation to precisely measure the robot’s relative translations and rotations, respectively, between build locations.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.194

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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 designOther design
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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEarth and Space 2016Same topicModular Robots and Swarm IntelligenceFrench-language works237,207