Quality Amplification of Error Prone Navigation for Swarms of Micro Aerial Vehicles
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
We present an error tolerant path planning algorithm for Micro Aerial Vehicle (MAV) swarms. It is GPS-free. The MAVs find their way using cameras to identify a series of visual landmarks. The landmarks lead towards the destination. MAVs are unaware of the terrain and landmark locations. Landmarks hold a-priori information whose interpretation is prone to errors. We distinguish two types of errors: recognition and advice. Recognition errors are due to misinterpretation of sensed data or a-priori information, or confusion of objects. Advice errors are due to outdated or wrong information associated to the landmarks. The MAVs cooperate and exchange information wirelessly, to minimize the errors. Consequently, the swarm experiences data quality amplification and error reduction. Quality amplification is related to the number of MAVs. The solution effectively achieves an adaptive error tolerant navigation system.
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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 | 0.000 |
| 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