Tactic Switching For Multiple UAV Teams via Model Predictive Control
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".