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Record W2096782936 · doi:10.1115/1.4030273

Optimal Design of Safe Planar Manipulators Using Passive Torque Limiters

2015· article· en· W2096782936 on OpenAlexafffund
Meiying Zhang, Clément Gosselin

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2015
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTorqueRobotLimiterIsotropySerial manipulatorPlanarContact forceControl theory (sociology)Slip (aerodynamics)Robot end effectorEngineeringComputer scienceTorque limiterControl engineeringMechanical engineeringParallel manipulatorArtificial intelligencePhysicsElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper presents a synthesis approach to build safe planar serial robotic mechanisms for applications in human–robot cooperation. The basic concept consists in using torque limiting devices that slip when a prescribed torque is exceeded so that the maximum force and the maximum power that the robot can apply to its environment are limited. In order to alleviate the effect of the change of pose of the robot on the joint to Cartesian force mapping, it is proposed to include more torque limiters than actuated joints. The design of isotropic force modules is addressed in order to produce proper force capabilities while ensuring safety. The proposed isotropic module of torque limiting devices leads to such characteristics. In addition to modeling the contact forces at the end-effector, the forces that can be applied by the robot to its environment when contact is taking place elsewhere along its links are also analyzed as well as the power of potential collisions. Examples of manipulator architectures and their static analysis are given. Finally, the design of a spatial serial manipulator using the isotropic planar force modules developed in the paper is illustrated.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.308
Threshold uncertainty score0.381

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 head0.036
GPT teacher head0.232
Teacher spread0.196 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations8
Published2015
Admission routes2
Has abstractyes

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