An Optimal Orthogonal Recharging Route Planner: A Multi-Robots, Multi-Rendezvous Recharging Scheme
Bibliographic record
Abstract
The issue of recharging a group of worker robots in their working environment has been tackled. For this purpose, a special purpose tanker robot has been devised with a planner, capable of generating recharging route that minimizes the cumulative sum of orthogonal distances of worker robots from their current locations to their corresponding recharging rendezvous locations along the recharging route (hence the term Orthogonal Recharging Route or ORR Planner). It has been proven that the ORR planner will result into a recharging route that minimizes the total worker robots distance traversal for recharging, irrespective of location of charging station/tanker. Experiments have been conducted to examine the practicality of the technique in contrast with scenarios of fixed charging station, as well as results of previous work based on Ordinary and Weighted Least Squares (OLS and WLS respectively) regressions. Results obtained in simulations are provided for illustrative comparison purpose among the different techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".