Multimodal Sensor Fusion and Adaptive Coordination Algorithms for Swarm Robotics in Disaster Response Environments.
Bibliographic record
Abstract
The increasing frequency of natural and man-made disasters highlights the urgent need for efficient response systems capable of navigating complex and hazardous environments. Swarm robotics, combined with advanced multimodal sensor fusion and adaptive coordination algorithms, offers a novel approach to addressing these challenges. This research explores the integration of diverse sensor modalities—such as thermal imaging, LiDAR, and acoustic data—into swarm robotic systems to improve real-time situational awareness and decision-making. Furthermore, we propose an adaptive coordination framework that optimizes robotic deployment, energy usage, and communication during disaster missions. Through a combination of simulations and physical experiments, the proposed system demonstrates notable advancements in victim detection accuracy, environmental mapping, and energy efficiency compared to existing methodologies. The findings of this study present a scalable and effective solution for deploying robotic swarms in disaster response scenarios, offering significant contributions to the fields of robotics and emergency management.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".