Research on Obstacle Avoidance Control of Multiple UAV Formation based on Genetic Algorithm
Bibliographic record
Abstract
The UAV (Unmanned Aerial Vehicle) group cooperative formation flight technology has the advantages of wide coverage, large activity radius, strong overall search ability and high efficiency of the aircraft group. Therefore, it is suitable for various complex tasks in the military field such as battlefield environment reconnaissance, tactical attack and cooperative search. This paper proposes a consistency control strategy based on GA (Genetic Algorithm) and applies it to multi-UAV formation obstacle avoidance, which can effectively solve the collision between UAVs and between UAV formation and obstacles. Demodulate the received ground desired control command, and introduce an additional auxiliary traction acceleration through GA to avoid the local optimal solution. The auxiliary traction acceleration is related to the speed and relative position of UAV and obstacles. It can be used as a disturbance to solve the local optimal solution, and also as an auxiliary acceleration to improve the speed of avoiding moving obstacles. Finally, the rapid formation and obstacle avoidance of UAV fleet during flight are realized, and the survivability of the fleet in the battlefield environment is improved.
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How this classification was reachedexpand
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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".