A Multirobot Path-Planning Strategy for Autonomous Wilderness Search and Rescue
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel strategy for the on-line planning of optimal motion-paths for a team of autonomous ground robots engaged in wilderness search and rescue (WiSAR). The proposed strategy, which forms part of an overall multirobot coordination (MRC) methodology, addresses the dynamic nature of WiSAR by: 1) planning initial, time-optimal, and piecewise polynomial paths for all robots; 2) implementing and regularly evaluating the optimality of the paths through a set of checks that gauge feasibility of path-completion within the available time; and 3) replanning paths, on-line, whenever deemed necessary. The fundamental principle of maintaining the optimal deployment of the robots throughout the search guides the MRC methodology. The proposed path-planning strategy is illustrated through a simulated realistic WiSAR example, and compared to an alternative, nonprobabilistic approach.
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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