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Record W2097679637 · doi:10.1109/robot.1990.126017

Manipulator transition to and from contact tasks: a discontinuous control approach

2002· article· en· W2097679637 on OpenAlexaff
James K. Mills

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Lyapunov functionWork (physics)Computer scienceMotion controlExponential stabilityRobotLyapunov stabilityMotion (physics)Coordinate systemRobot end effectorControl systemStability (learning theory)Dynamical systems theoryControl engineeringNonlinear systemEngineeringArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

The stability and control of robotic manipulators during the execution of tasks that require the manipulator to make a transition from noncontact motion to contact motion, or vice versa, are investigated. A dynamic model of the manipulator during noncontact and contact motion is developed. This model includes the effect of the inevitable collision that occurs between the manipulator end effector and the work environment during the transition from noncontact to contact motion. The work environment that the manipulator comes into contact with is modeled as a very still surface. The dynamic model of the robot during this transition is transformed through a nonlinear coordinate transformation into a new set of generalized coordinates in which the form of the dynamics is greatly simplified. A discontinuous control is proposed for the robotic manipulator system. It is shown that with this discontinuous control applied to the system, the closed-loop system can be treated as a generalized dynamical system. Using the theory associated with generalized dynamical systems, it is possible to extend Lyapunov stability analysis to systems with discontinuous controls. The system dynamics is written as a contingent equation to which a set valued control function is applied. Within this mathematical framework, the uniform asymptotic stability in the larger of the closed-loop systems is proved. The controller has several desirable properties, including the ability to return to contact motion if the manipulator end effector inadvertently leaves the surface due to some external disturbance acting on the system.>

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.015
GPT teacher head0.186
Teacher spread0.171 · 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 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

Citations63
Published2002
Admission routes1
Has abstractyes

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