Cooperative Multi-AAV Path Planning for Discovering and Tracking Multiple Radio-Tagged Targets
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
Discovering and tracking wildlife targets are essential for gaining insights into the behavioral patterns and habits of animals within their natural habitats. With low cost and high maneuverability, mini autonomous aerial vehicles (AAVs) can achieve robust and rapid locating and tracking of multiple targets through collaboration. This work proposes a method for multitarget task allocation and path planning for AAV swarms, addressing the challenges of locating and tracking multiple wildlife with very high frequency (VHF) radio tags while avoiding potential disturbances to the wildlife. Our approach proposes a layered framework for the multi-AAV multitarget wildlife tracking problem: 1) the state estimation layer performs fast receiver signal strength indicator (RSSI) signal acquisition and employs the particle filtering algorithm to localize targets’ positions; 2) the task assignment layer uses a quadratic allocation method for AAVs’ real-time target allocation, starting with reasonable initial target sets via mixed-integer programming and efficiently readjusting targets based on real-time environment; and 3) the motion planning layer introduces an optimization-based approach to generate smooth and executable trajectories that can simultaneously ensure desired safe distances from objects of interest. Simulation experiments validate the effectiveness of the obtained AAV swarm tracking scheme.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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