Canine Assisted Robot Deployment for Urban Search and Rescue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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