Event-Triggered Control for Autonomous Detection and Treatment of Membrane Lesions using Microrobot Swarms
Bibliographic record
Abstract
Recent advances in robotics have expanded the potential of microrobot swarms (MRSs) in medicine, yet clinical deployment remains limited due to reliance on non-autonomous systems. This study proposes an event-triggered distributed coverage control framework that enables MRSs to autonomously detect and treat membrane lesions. To model lesion dynamics accurately, we introduce a coupled reaction-diffusion equation and a Hawkes process that capture spatial spread and temporal emergence. This model informs a modified Lloyd algorithm to guide MRSs toward the centroids of Voronoi cells, optimizing drug release over pre-existing lesion areas. Furthermore, we design an event-triggered mechanism prioritizing treatment of newly emerging lesions, redirecting microrobots to lesion centers for prioritized response. This adaptive framework effectively addresses lesion proliferation and promotes membrane healing. Simulations demonstrate improved coverage efficiency and lesion containment compared to conventional strategies.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".