Canine Assisted Robot Deployment for Urban Search and Rescue
Bibliographic record
Abstract
In Urban Search and Rescue (USAR) operations the search for survivors must occur before rescue operations can proceed. Two methods that can be used to search in rubble are trained search dogs and specialized response robots (sometimes called rescue robots). Rescue robots are used to collect information about trapped people within a disaster like a collapsed building. Information from them can help first responders plan and execute a rescue effort. The main challenge for these robots is the restrictions placed on their mobility by challenging rubble surfaces. While current research in this area attacks this challenge through mechanical design, good solutions remain elusive. This paper presents a new method for dispersing response robots called Canine Assisted Robot Deployment (CARD). CARD's approach utilizes USAR dogs to deliver robots close to a trapped human detected by the dog. This method exploits the canine ability to find survivors using their olfactory sensors and agility. Once a dog carrying a small robot has found a casualty, the robot can be dropped and begin exploring. Initial experiments and results are described in this paper.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".