A Dynamic Path Generation Method for a UAV Swarm in the Urban Environment
Bibliographic record
Abstract
In this paper, a dynamic path planning method for swarming of unmanned aerial vehicles (UAVs) in the urban environment is presented. For the missions of a team of UAVs in a complex environment, conflict-free paths for all vehicles should be calculated dynamically in real-time using the latest information on the changes in the surroundings such as pop-up threats. Therefore, we propose a hierarchical dynamic path planner that consists of a offline path planning and a real-time model predictive trajectory generator. In this framework, many existing and proven off-line algorithms can be deployed for globally optimal path planning. The pre-computed trajectory is sent to the model predictive layer, which generates locally feasible trajectory free from conflicts. In this manner, each vehicle is able to fly along its designated path to reach the destination while avoiding obstacles or vehicles in collision path with minimal deviation from the designated path. For validation, the proposed algorithm is applied to a deployment scenario of sixteen rotary-wing UAVs flying in a cluttered urban area and showed a satisfactory performance. I.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".