Optimal deployment of robotic teams for autonomous wilderness 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
This paper presents a novel method for the optimal deployment of multi-robot teams for autonomous, coordinated wilderness search and rescue. The new concept of iso-probability curves, used to represent the time-varying prediction of a lost person's probable location within the search area, is utilized to effectively distribute the search effort. The proposed method can be used for initial deployment, as well as subsequent on-line re-deployment to address the dynamic nature of the search for a moving lost person in a growing search area with varying terrain. The modularity of the proposed method allows the user to define and utilize different objective functions and weigh them according to the goal at hand. The two specific objective functions considered in this paper are (minimizing) search time and (maximizing) the probability of success. A simulated realistic wilderness search scenario demonstrates the integration of optimal deployment within the overall search methodology.
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 | 0.001 | 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 | 0.001 | 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