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Record W2144529183 · doi:10.1109/iros.2009.5354034

On the feasibility and suitability of MR and ER based actuators in human friendly manipulators

2009· article· en· W2144529183 on OpenAlexaff
Alex S. Shafer, Mehrdad R. Kermani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsWestern University
Fundersnot available
KeywordsActuatorRobotRoboticsInertiaControl engineeringComputer scienceTorqueServomechanismField (mathematics)ServomotorIntrinsic safetySet (abstract data type)MechatronicsArtificial intelligenceMechanical engineeringEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

For several decades now, the robotics industry has postulated the emergence of a new breed of manipulators capable of safely interacting with humans. The integration of robots into human environments has always possessed a unique set of challenges. The primary concern has always been for the safety of the human coworkers. Insuring their safety has led to the desire to design manipulators with intrinsic safety characteristics. Specifically, it is noted that modern `human-safe' manipulators are successful if they have minimized both their weight and reflected actuator inertia. The leading research accomplishing this relies on complex actuation systems. Such systems commonly suffer from degraded performance and high cost of development. Magneto- and electro-rheological fluids are a class of smart-materials that can instantaneously, and reversibly alter their rheological properties under the influence of an applied field. Devices developed with such fluids are known to possess superior torque-to-inertia characteristics over conventional servo systems. Moreover, their simple mechanical construction suggests that manipulators using such devices could be developed at a lower cost. In this paper, we will discuss the potential benefits rheological fluids can bring to the field of human friendly manipulators.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.214

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 head0.018
GPT teacher head0.248
Teacher spread0.231 · 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 designObservational
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

Citations23
Published2009
Admission routes1
Has abstractyes

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