Obstacle Avoidance of Swarms Using Pinning Control
Why this work is in the frame
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Bibliographic record
Abstract
In swarm control tasks, local objectives, on each agent, can interfere with the group’s collective objectives. For example, in an environment with obstacles, the motion of the whole group can be affected by local obstacle encounters (happening in a few agents). In this work, we investigate the navigation of swarms in the presence of obstacles. We propose a novel control strategy to avoid obstacles while reducing swarm fragmentation, i.e., limiting the division of the swarm into disconnected groups. We model the swarm as a network where each vehicle is topologically connected with the neighbours that are within the agent’s sensing range. We actively monitor the agents’ connections in order to identify the necessity of redesigning the network, splitting a larger group into groups with fewer agents. Also, we use a path planning algorithm to provide the trajectory to guide the agents to the final destination. At the end of this paper, we show the results of simulation trials to demonstrate the performance of our control strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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