OBSTACLE AVOIDANCE FOR MULTI-UAV SYSTEM WITH OPTIMIZED ARTIFICIAL POTENTIAL FIELD ALGORITHM
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have incomparable advantages and gradually form multi-UAV systems, in which many UAVs work together to accomplish tasks through cooperation.In the case of flying in an unknown environment and the very close distance between them, it is essential to have a useful collision avoidance system to avoid the collision between obstacles and UAVs and between inter-UAVs.In this paper, a comprehensive optimal obstacle avoidant mechanism of UAV path planning is constructed.The flight environment of UAVs is described, and the warning ranges and danger ranges of UAV and obstacles are given fully considering the execution time and flight platform of UAV.Then, a reliable artificial potential field (APF) model for path planning of multi-UAV systems in a complex environment is presented, in which a method to save UAV's energy and the solution for UAV Local minimization problem are proposed.Finally, the applicability of the improved algorithm is verified by simulation experiments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".