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Record W2140614262 · doi:10.1109/ccece.2008.4564717

Design of foam covering for robotic arms to ensure human safety

2008· article· en· W2140614262 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
TopicRobot Manipulation and Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRobotTorsoHuman–robot interactionStiffnessContact forceSimulationImpactComputer scienceCoupling (piping)Human headMechanical engineeringEngineeringStructural engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Unintentional physical human-robot contact is becoming more common as robots operate in closer proximity to people. This contact may generate a large impact force and cause severe human injuries. Therefore, the ability to reduce the human-robot impact force and ensure human safety is a fundamental requirement for human-friendly robots. An easy and effective way to achieve this is using foam to cover the robot surface. We present a method for designing the stiffness and thickness of the foam covering based on a realistic safety threshold and an improved impact force model. Our model incorporates the previously neglected coupling of the human head to the torso and the coupling of the robot arm to its base. The impact model and model-based design procedure are experimentally verified for various foam properties, and robot and human velocities. The impact experiments are performed with an apparatus simulating the human head and, at lower velocity, with a human volunteer. The maximum error between the predicted and experimental peak impact force results is 8%.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
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 head0.038
GPT teacher head0.210
Teacher spread0.173 · 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