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Record W2197483363 · doi:10.1109/icrom.2015.7367813

Adaptive estimation of robot environmental force interacting with soft tissues

2015· article· en· W2197483363 on OpenAlex
Maryam Sharifi, Heidar Ali Talebi, Masoud Shafiee

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsControl theory (sociology)Lyapunov functionLemma (botany)Convergence (economics)Position (finance)Computer scienceRobotStability (learning theory)Tracking (education)Function (biology)Control engineeringAdaptive controlTracking errorRobot end effectorControl (management)EngineeringArtificial intelligencePhysicsNonlinear system

Abstract

fetched live from OpenAlex

In this paper, position control of a robot's end effector, by proposing an estimation mechanism for the interaction force between robotic tools and a soft tissue is presented. The proposed approach is using an adaptive strategy to provide the estimated force for achieving convergence of position and velocity tracking errors to zero. Moreover, the global stability of the closed loop system, using a time varying Lyapunov function and Barbalat's lemma is investigated. The proposed scheme considers a dynamic model for the environment. For this purpose, a viscoelastic model is adopted for the environment rather than an elastic one. This assumption has a great impact on effectiveness of all control strategies that use the force feedback, when working with soft tissues. Moreover, simulation results are given to demonstrate the effectiveness of the proposed approach.

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 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: none
Teacher disagreement score0.825
Threshold uncertainty score0.158

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 head0.225
Teacher spread0.208 · 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

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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