Self-adaptive pattern formation with battery-powered robot swarms
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a distributed, energy-aware algorithm for an autonomous deployment of battery-powered robots in a specified pattern. While each robot gradually discharges and leaves the formation to recharge, the algorithm presented in this paper assures that the formation pattern is preserved. This is achieved by defining the desired pattern as a point cloud where each point is occupied by a robot. The point cloud is transformed into a tree model that is shared among all robots. This model is used by each robot independently to govern its behavior, resulting in a self-adaptive network of robots which automatically generate paths for joining the formation and leaving it to recharge. Robots which leave the formation are replaced by neighbors to preserve the formation pattern. To demonstrate our algorithm we use a physics-based simulator and evaluate the persistence of the pattern formation formed by a robot swarm in an environment without global positioning, using only range and bearing.
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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