Improving Search and Rescue Using Contextual Information
Bibliographic record
Abstract
Search and rescue (SAR) is a challenging application for autonomous robotics research. The requirements of this kind of application are very demanding and are still far from being met. One of the most compelling requirements is the capability of robots to adapt their functionalities to harsh and heterogeneous environments. In order to meet this requirement, it is common to embed contextual knowledge into robotic modules. We have previously developed a context-based architecture that decouples contextual knowledge, and its use, from typical robotic functionalities. In this paper, we show how it is possible to use this approach to enhance the performance of a robotic system involved in SAR missions. In particular, we provide a case study on exploration and victim detection tasks, specifically tailored to a given SAR mission. Moreover, we extend our contextual knowledge formalism in order to manage complex rules that deal with spatial and temporal aspects that are needed to model mission requirements. The approach has been validated through several experiments that show the effectiveness of the presented methodology for SAR.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".