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Record W2110912067 · doi:10.1109/iros.2009.5354333

Dual-master teleoperation control of kinematically redundant robotic slave manipulators

2009· article· en· W2110912067 on OpenAlexaff
Pawel Malysz, Shahin Sirouspour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTeleoperationRedundancy (engineering)Master/slaveRobotComputer scienceControl theory (sociology)Robotic armTeleroboticsKinematicsControl engineeringArtificial intelligenceEngineeringControl (management)Mobile robot

Abstract

fetched live from OpenAlex

Kinematically redundant robotic manipulators (KRRM) can provide a great degree of flexibility for working in complex unstructured environments. Teleoperation control of KRRM requires a strategy to resolve the redundancy of the slave robot while achieving transparency in the task space. In this paper, a two-master control approach is proposed in which the first master transparently controls the redundant slave end-effector in the task space, denoted as the primary task. Meanwhile, a second master exploits the slave redundancy to perform a secondary task such as obstacle avoidance or internal position control. Kinematic redundancy is considered for the slave robot and the traditional autonomous null-space control approach is also accommodated. Teleoperation control is achieved in two steps. First, velocity-level redundancy resolution is attained through new joint-space Lyapunov-based adaptive motion/force controllers. Coordinating reference commands for the joint-space controllers are designed to give priority to the primary task and decoupling between the tasks is achieved without the use of a dynamically consistent pseudo-inverse. Experimental results with two identical planar two-degree-of-freedom master devices controlling a simulated four-degree-of-freedom redundant slave robot show the effectiveness of the 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.

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: none
Teacher disagreement score0.795
Threshold uncertainty score0.420

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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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

Citations24
Published2009
Admission routes1
Has abstractyes

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