A Bionic Dynamic Path Planning Algorithm of the Micro UAV Based on the Fusion of Deep Neural Network Optimization/Filtering and Hawk-Eye Vision
Bibliographic record
Abstract
A micro unmanned aerial vehicle (UAV) only equipped with a monocular camera is hard to accomplish a flying task with obstacles avoidance and target tracking simultaneously. In this article, a bionic dynamic path planning algorithm was developed for cooperation of obstacles avoidance and target tracking. An improved bat algorithm (BA) optimized transfer learning convolutional neural network (CNN) and bio-inspired optical flow balance algorithm was combined for obstacles avoidance. The Hawk-eye algorithm with line of sight (LOS) tracking rules is aimed at UAV dynamic tracking with obstacles avoidance. All of perception information, including avoidance and tracking were fused in UAV motion decision phase. The experiments include “obstacles avoidance” and “obstacles avoidance + target tracking” parts. Comparing with manual control and other algorithms, the bionic dynamic path planning algorithm in this article showed certain advantages in success rate, less obstacles collisions, and less major accidents.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".