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Record W2074502866 · doi:10.1115/imece2004-61201

Wire Deactivation Methodology for Inverse Dynamics of Wire-Actuated Redundant Manipulators

2004· article· en· W2074502866 on OpenAlexaff
Amin Kamalzadeh, Leila Notash

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsTorqueControl theory (sociology)ActuatorRevolute jointRedundancy (engineering)Inverse dynamicsKinematicsSerial manipulatorWrenchRobot end effectorTrajectoryComputer scienceRobotParallel manipulatorEngineeringPhysicsMechanical engineeringClassical mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

Wire-actuated robot manipulators are generally lighter than other manipulators as actuated wires are used instead of joint actuators. The inverse dynamic modeling of these manipulators is complicated by the existence of multiple kinematic constraints as well as redundancy in actuation. In wire-actuated parallel manipulators with a constraining linkage and in tendon-driven serial manipulators, wires are used to control the joints. In these manipulators, each wire can provide a torque/force on a link about/along its revolute/prismatic passive joint in one direction, as wires only act in tension. Using one wire for each link sometimes does not fully constrain the motion of the link about/along its passive joint. Therefore, a second wire is attached to some links in a “counterbalance” configuration; i.e., the second wire can provide a “complementary” torque/force in the opposite direction of the torque/force produced by the first wire on the link about/along its passive joint. Depending on the end effector trajectory and external force at each instant, one of the mentioned two wires provides the desired direction of torque/force and the other, “counteracting wire,” imposes a “counteracting” torque/force on the link about/along its passive joint. Using more actuators than degrees of freedom (DOF) in the manipulator causes redundancy in actuation, which means that for a unique end effector trajectory and external force, inverse dynamic results (actuator torques/forces) have infinite solutions within a null space of actuator torques/forces. Obtaining a unique result within the null space requires several considerations, such as avoiding negative tensions in wires and decreasing the actuator torques/forces. The purpose of this article is to find a methodology to limit the infinite inverse dynamic solutions to one while the negative wire tensions are avoided and actuator torques/forces are relatively decreased. As explained in this article, by reducing the counteracting wire tensions, other actuator torques/forces are decreased, because a portion of other actuator torques/forces neutralizes the tensions of counteracting wires. A methodology is developed to detect the counteracting wires in real-time and to present the corresponding tensions to a low positive value; i.e., the counteracting wires are “deactivated.” The proposed methodology can be implemented in the inverse dynamic modeling of wire-actuated parallel manipulators with a constraining linkage and tendon-driven serial manipulators via using the Lagrangian method. This methodology can be used to provide optimum actuator torques/forces and avoid negative tensions in actuated wires. The methodology is implemented in the inverse dynamic modeling of a 4-DOF wire-actuated manipulator where there is one degree of actuation redundancy. In the simulation results, the inverse dynamic model based on the proposed methodology is observed to be quite robust in terms of avoiding negative wire tensions by deactivating the right actuated wire.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.048
GPT teacher head0.264
Teacher spread0.216 · 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
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

Citations0
Published2004
Admission routes1
Has abstractyes

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