LOST Highway: A Multiple-Lane Ant-Trail Algorithm to Reduce Congestion in Large-Population Multi-robot Systems
Why this work is in the frame
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Bibliographic record
Abstract
We propose a modification of a well-known ant-inspired trail-following algorithm to reduce congestion in multi-robot systems. Our method results in robots moving in multiple lanes towards their goal location. Our algorithm is inspired by the idea of building multiple-lane highways to mitigate traffic congestion in traffic engineering. We consider the resource transportation task where autonomous robots repeatedly transport goods between a food source and a nest in an initially unknown environment. To evaluate our algorithm, we perform simulation experiments in several environments with and without obstacles. Compared with the baseline SO-LOST algorithm, we find that our modified method increases the system throughput by up to 3.9 times by supporting a larger productive robot population.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it