From Swarms to Stars: Task Coverage in Robot Swarms with Connectivity Constraints
Why this work is in the frame
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Bibliographic record
Abstract
Swarm robotics carries the potential of solving complex tasks using simple devices. To do so, however, one must define distributed control algorithms capable of producing globally coordinated behaviours. We propose a methodology to address the problem of the spatial coverage of multiple tasks with a swarm of robots that must not lose global connectivity. Our methodology comprises two layers: (i) a distributed Robot Navigation Controller (RNC) is responsible for simultaneously guaranteeing connectivity and pursuit of multiple tasks; and (ii) a global Task Scheduling Controller approximates the optimal strategy for the RNC with minimal computational load. Our contributions include: (i) a qualitative analysis of the literature on connectivity assessment, (ii) our proposed methodology, (iii) simulations in a multi-physics environment, (iv) real-life robot experiments, and (v) the experimental validation of connectivity, coverage optimality, and fault-tolerance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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