Time-optimal rendezvous planning for pick-and-place task sharing
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".