Hierarchical Cooperative Assignment Algorithm (CAA) for mission and path planning of multiple fixed-wing UAVs based on maximum independent sets
Why this work is in the frame
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Bibliographic record
Abstract
Mission planning can be solved as a combinatorial optimization problem which involves computing the path and selecting the agents that will be assigned to a given task. In scenarios with multiple UAVs, the proper control of the vehicle to achieve the proposed path is also a relevant task. This paper proposes a solution to the mission planning problem that involves probabilistic search and optimization of path planning and a graph-based combinatorial solution of task assignment. In addition, we propose an invariant model predictive controller based on the SO(2) manifold to deal with the execution of UAV missions. Our results demonstrate that the algorithm is capable of assigning all agents to tasks and computing a viable and smooth trajectory for the UAVs to follow. Also, the control strategy is capable of guiding the vehicle through the trajectories generated from a start position to the task location.
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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