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Record W2120398872 · doi:10.1109/robot.2009.5152334

2 DOF cartesian force limiting device for safe physical human-robot interaction

2009· article· en· W2120398872 on OpenAlexaff
Nicolas Lauzier, M. Grenier, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRobot end effectorRobotMechanism (biology)Control theory (sociology)TorqueCollisionRevolute jointLimiterInertiaContact forceSimulationEngineeringComputer sciencePhysicsArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a device that significantly increases the safety level of suspended robots whose end-effector orientation remains constant with respect to the vertical direction (e.g. Scara-type suspended robots). The device is a two-degree-of-freedom (DOF) parallel mechanism with a parallepipedic architecture on which two revolute joints have been replaced with commercially available torque limiters. The device is implemented as a mechanical connection between the robot and the effector. It is rigid unless excessive horizontal forces are applied on the end-effector, for example during a collision. The level of force that activates the mechanism is set by properly adjusting the threshold of the torque limiters. Furthermore, a collision can be rapidly detected with a limit switch placed on one of the links of the mechanism and a signal can be sent directly to brakes that will stop the robot, without passing through a controller and thus improving the reliability and reaction-time of the safety system. By mechanically disconnecting the robot from its end-effector, the device ensures that the person involved in the collision is only subjected to the inertia of the end-effector and thus potential injuries are greatly reduced. A prototype of the proposed device has been built to validate the concept and to study its behaviour for collisions with different velocities and orientations.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.338

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 head0.290
Teacher spread0.272 · 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
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

Citations13
Published2009
Admission routes1
Has abstractyes

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