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Record W4248150365 · doi:10.1109/iros.2016.7759623

An anticipative kinematic limitation avoidance algorithm for collaborative robots: Two-dimensional case

2016· article· en· W4248150365 on OpenAlex

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
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsKinematicsRobotComputer scienceTask (project management)Obstacle avoidanceObstacleRobot kinematicsPosition (finance)Collision avoidancePrincipal (computer security)Artificial intelligenceSimulationControl theory (sociology)Control engineeringAlgorithmEngineeringMobile robotControl (management)Collision

Abstract

fetched live from OpenAlex

This paper presents an anticipative robot kinematic limitation avoidance algorithm for collaborative robots. The main objective is to improve the performance and the intuitivity of the physical human-robot interaction. One obstacle to achieve this goal is the management of limitations such as joint position limitation, singularities and collisions with the environment. Indeed, in addition to performing a given principal task, human users must pay a close attention to the manipulator configuration in order to handle the kinematic limitations. The proposed anticipative algorithm aims at relieving the human user from having to deal with such limitations. The algorithm is first presented and detailed for each individual limitation of a planar RR serial robot. The framework developed to manage several limitations occurring simultaneously is then presented. Finally, experiments are performed in order to assess the performance of the algorithm.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.836
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.317
Teacher spread0.281 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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