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Record W4386256222 · doi:10.24908/iqurcp16723

Origami, Shape Memory Alloys, and Geckos? Designing and Manufacturing an Underactuated Self-Reconfigurable Cube Robot

2023· article· en· W4386256222 on OpenAlexaffvenue
Carter Hyndman, Matthew Robertson

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsQueen's University
Fundersnot available
KeywordsActuatorRobotModular designHingeCube (algebra)Shape-memory alloySMA*RoboticsComputer scienceMorphingEngineeringArtificial intelligenceMechanical engineeringGeometryAlgorithm

Abstract

fetched live from OpenAlex

Modular self-reconfigurable robotic systems are an active field of research leading to versatile, robust, and compact robotic systems. One approach utilizes self-relocatable cuboid robots as building blocks to construct lattice-type modular robotic systems which can be used to construct structures in humanly inaccessible locations through remote or autonomous control. Previous work on self-assembling cube robots has been actuated through both fluidic and inertial methods to provide robot locomotion. The presented robotic cube functions through the harmony of an underactuated Origami Linear Soft Pneumatic Actuator (OL’ SPA) and the modulation of four Shape Memory Alloy (SMA) hinge pin actuators for each face of the cube. By disengaging three of the SMA hinge pin actuators on one of the cube’s faces, the underactuated OL’ SPA expands circumferentially about the remaining engaged SMA hinge pin actuator, opening the cube’s face. Applied to the outside of the outer cube face is a bioinspired gecko tape adhesive, allowing the cube robot the ability to self-reconfigure and climb. Through this innovative integration of novel actuation mechanisms and bioinspired adhesion, the presented robotic cube navigates uncharted terrain in the realm of modular self-reconfiguring robotics.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.330
Teacher spread0.237 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueInquiry Queen s Undergraduate Research Conference ProceedingsSame topicModular Robots and Swarm IntelligenceFrench-language works237,207