Cooperative Multi-Vehicle Search and Coverage Problem in an Uncertain Environment
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
A distributed approach is proposed in this paper to address a cooperative multi-vehicle search and coverage problem in an uncertain environment such as forest fires monitoring and detection. Two different types of vehicles are used for search and coverage tasks: search and service vehicles. The search vehicles have a priori probability maps of targets in the environment. These vehicles update the probability maps based on their sensors measurements during the search mission. The search vehicles use a limited look-ahead dynamic programming algorithm to find their own path individually while their objective is to maximize the amount of information gathered by the whole team. The task of the service vehicles is to optimally spread out over the environment to cover the interested area for a mission. A Voronoi-based coverage control strategy is proposed to modify the configuration of service vehicles in such a way that a prescribed coverage cost function is minimized using the updated probability maps which are provided by the search vehicles. The improved performance of the proposed approach compared to conventional coverage methods is demonstrated by numerical simulation and experimental results.
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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 | 0.001 | 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 | 0.001 |
| 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