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Record W2152059006 · doi:10.1109/ccece.2008.4564692

Docking joint for autonomous self-assembly

2008· article· en· W2152059006 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsWestern University
Fundersnot available
KeywordsRobotModular designComputer scienceSelf-reconfiguring modular robotDistributed computingMobile robotTask (project management)Control engineeringEmbedded systemRobot controlArtificial intelligenceEngineeringSystems engineeringProgramming language

Abstract

fetched live from OpenAlex

Automatic docking between separate parts is a fundamental challenge that arises in engineering systems which autonomously change their structures. This ability enables free bodies in the same environment to join together in order to complete a task that would otherwise not be achievable with the independent modules. Docking capability is highly desirable in applications such as self-reconfigurable robots, autonomous undersea vehicles, and automated recharging of security robots. The major problem for this task is to overcome alignment errors and ensure a firm connection. We have developed a mechanism to overcome this challenging problem. This paper focuses on the design details of our proposed docking joint and presents the results of finite element analysis (FEA) on the joint as well as experimental results. Our final goal is to develop an autonomous multi-rove robot. In this concept, a number of independent mobile robots can self-assemble into a single modular robot.

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 categoriesMeta-epidemiology (narrow)
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 score0.940
Threshold uncertainty score1.000

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 head0.024
GPT teacher head0.190
Teacher spread0.166 · 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