Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a distributed multi-robot collision avoidance algorithm based on the concept of Buffered Voronoi Cells (BVC). We propose a novel algorithm for avoiding deadlocks consisting of three stages: deadlock prediction, deadlock recovery, and deadlock recovery success prediction. Simple heuristics (such as the right-hand rule) are often used to avoid deadlocks. Such heuristics might reduce deadlock in simple configurations and sparsely populated environments, but they begin to fail in complex configurations and more densely populated environments. We evaluate the performance of our algorithm using an open-source web-based multi-robot simulation. The results show that while the proposed algorithm does not eliminate the occurrence of deadlocks, it drastically reduces their occurrence, and leads to a considerable improvement in performance, especially in high-density environments. We also validate the real-world performance of the proposed algorithm in live experiments.
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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 | 0.001 | 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