MétaCan
Menu
Back to cohort
Record W2167006817 · doi:10.5194/ms-2-9-2011

Design and performance assessment of an underactuated hand for industrial applications

2011· article· en· W2167006817 on OpenAlexaboutno aff
C. Meijneke, Gert A. Kragten, Martijn Wisse

Bibliographic record

VenueMechanical sciences · 2011
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsUnderactuationPayload (computing)GRASPRange (aeronautics)Control engineeringActuatorEngineeringComputer scienceControl (management)Control theory (sociology)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract. The Delft Hand 2 (DH-2) is an underactuated robot hand meant for industrial applications, having six degrees of freedom (DoF), one actuator (DoA) and no sensors. It was designed to provide a cheap and robust hand to grasp a large range of objects without damaging them. The goal of this paper is to assess the design and performance of the DH-2, demonstrating how the design was optimized for its intended application area and how the hand was simplified to make it commercially attractive. Performance tests show that the DH-2 has a payload of 2 kg for an object range of 60 to 120 mm, it can close or open within 0.5 s, and it only uses open-loop control by means of the input voltage of the motor. The results demonstrate that the industrial need of a simple, cheap and effective robotic hand can be achieved with the principle of underactuation and the use of conventional components. This paper was presented at the IFToMM/ASME International Workshop on Underactuated Grasping (UG2010), 19 August 2010, Montréal, Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.211
GPT teacher head0.321
Teacher spread0.110 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations29
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMechanical sciencesSame topicRobot Manipulation and LearningFrench-language works237,207