Tactic Switching For Multiple UAV Teams via Model Predictive Control
Why this work is in the frame
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Bibliographic record
Abstract
Intelligent and flexible control strategies are required to allow teams of Unmanned Aerial Vehicles (UAVs) to cooperate in order to accomplish a multitude of challenging group tasks. Decentralized Model Predictive Control (MPC) is used to solve the problem of tactic switching for two teams of N UAVs. The UAVs teams accomplish a desired formation tactic, assign themselves to a desired target and then switch to dynamic encirclement around the chosen target. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of the desired formation approach, while a combination of decentralized LMPC and Feedback Linearization (FL) is applied on the teams to accomplish dynamic encirclement tactic. The main contribution of this paper lies inthe use of MPC policy to solve the tactic switching problem of two UAV teams around several targets while ensuring the stability and robustness of the system during the simulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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