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Record W2125690420 · doi:10.1109/robot.2000.846436

Time-optimal rendezvous planning for pick-and-place task sharing

2002· article· en· W2125690420 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 institutionsSimon Fraser University
Fundersnot available
KeywordsRendezvousTask (project management)WorkspaceComputer scienceRobotSMT placement equipmentRelayPoint (geometry)Mode (computer interface)Key (lock)SimulationArtificial intelligenceEngineeringHuman–computer interactionMathematics

Abstract

fetched live from OpenAlex

We propose a mode-sequential task sharing-for co-operating robot manipulators carrying out a pick-and-place task sharing in a common workspace. As the name implies, in this mode, each individual robot completes part of the same task. The first manipulator picks up the part(s) and directly passes it over to the second manipulator (like a baton being passed from one runner to the other in a relay race), which completes the task by placing the part at its desired goal location. The point at which the transition, i.e., passing over the part, occurs is the rendezvous point. We analyse this approach to minimize the total task time subject to dynamic constraints of the robots. A key step is to determine the optimal rendezvous point (ORP) that results in the optimal task time. We present an algorithm to determine the ORP and show that our approach results in a speed up by a factor of more than two over the conventional single manipulator case.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.482
Threshold uncertainty score0.629

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 science0.0010.000
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 head0.035
GPT teacher head0.257
Teacher spread0.222 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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