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Record W2161590836 · doi:10.1109/robot.1993.291959

Real-time collision avoidance of robot manipulators for unstructured environments

2002· article· en· W2161590836 on OpenAlexaff
S. Kalaycioglu, Murat Tandirci, D.S. Nesculescu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsCanadian Space AgencyUniversity of Waterloo
Fundersnot available
KeywordsCollision avoidanceRobotObstacle avoidanceInverse kinematicsTrajectoryKinematicsComputer scienceJacobian matrix and determinantControl theory (sociology)Forward kinematicsFunction (biology)Artificial intelligenceCollisionMathematicsMobile robotControl (management)

Abstract

fetched live from OpenAlex

A real-time collision avoidance approach for complete posture (all links) of a robot manipulator in an unstructured environment based on a modified impedance control concept is presented. Instead of high-level trajectory planning and an offline collision avoidance approach as used in conventional methods, robot trajectories are generated in operational space. Multiple moving obstacles are avoided in real-time as part of the lower level control task. The concept is very suitable for real-time applications since the defined impedances are linear and can be obtained directly using robot kinematics variables without an inverse kinematics function. Moreover, the impedance between the obstacle and any point on the robot arm utilizes the linearity property of the Jacobian function and can be calculated by using a simple geometric relation. The problem of local singular configurations is eliminated by this technique.>

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 score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.016
GPT teacher head0.203
Teacher spread0.186 · 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

Citations9
Published2002
Admission routes1
Has abstractyes

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