Biconnecting a Network of Mobile Robots Using Virtual Angular Forces
Bibliographic record
Abstract
This paper proposes a new solution to the problem of self-deploying a network of wireless mobile robots with simultaneous consideration to several criteria, that are, the fault-tolerance (biconnectivity) of the resulting network, its coverage, its diameter, and the quantity of movement required to complete the deployment. These criteria have already been addressed individually in previous works, but we propose here an elegant solution to address all of them at once. Our approach is based on combining two complementary sets of virtual forces: spring forces, whose properties are well known to provide optimal coverage at reasonable movement cost, and angular forces, a new type of force proposed here whose effect is to rotate two angularly consecutive neighbors toward one another when the corresponding angle is larger than 60° (even if these nodes are not direct neighbors). Angular forces have the global effect of biconnecting the network and reducing its diameter, while not affecting the benefits obtained by spring forces on coverage. In this paper we give a detailed description of the combination of both types of forces. We also provide an implementation relying only on position exchanges within two hops. Simulations results are finally presented to evaluate our solution with respect to the four considered criteria (coverage, biconnectivity, quantity of movements, and diameter), and compare it with prior approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".