Restoring Connectivity in Robotic Swarms – A Probabilistic Approach
Bibliographic record
Abstract
Abstract Connectivity is an integral trait for swarm robotic systems to enable effective collaboration between the robots in the swarm. However, connectivity can be lost due to events that could not have been a priori accounted for. This paper presents a novel probabilistic connectivity-restoration strategy for swarms with limited communication capabilities. Namely, it is assumed that the swarm comprises a group offollowerrobots whose global connectivity to a base can only be achieved via a localizedleaderrobot. In this context, the proposed strategy incrementally restores swarm connectivity by searching for the lost robots in regions-of-interest (RoIs) determined using probability theory. Once detected, newly found robots are either recruited to help the leader in the restoration process, or directly guided to their respective destinations through accurate localization and corrective motion commands. The proposed swarm-connectivity strategy, thus, comprises the following three stages: (i) identifying a discrete set of optimal RoIs, (ii) visitation of these RoIs, by the leader robot, via an optimal inter-region search path, and (iii) searching for lost robots within the individual RoIs via an optimal intra-region search path. The strategy is novel in its use of a probabilistic approach to guide the leader robot’s search as well as the potential recruitment of detected lost robots to help in the restoration process. The effectiveness of the proposed probabilistic swarm connectivity-restoration strategy is represented, herein, through a detailed simulated experiment. The significant efficiency of the strategy is also illustrated numerically via a comparison to a competing random-walk based method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".