Research on trajectory planning and control of flying robotic arm based on image moments and deep learning vector product
Bibliographic record
Abstract
With the development of science and technology, the application of flight manipulators has received extensive attention.The flying manipulator has broad application prospects, such as the maintenance of high-voltage towers, the storage and retrieval of elevated goods in warehouses, and the delivery of express and takeout goods.Before the actual application of the flight manipulator, due to the complex task requirements and nonlinear environment, it is necessary to continuously optimize the Trajectory Planning and Control (hereinafter referred to as TPC) of the flight manipulator.In order to improve the recognition and positioning accuracy of the robotic arm on the surface of the aircraft, and achieve precise control of the autonomous motion and operation of the robotic arm on the surface of the aircraft, this paper studies the TPC of the flight robotic arm based on deep learning, image moment and vector product methods, establishes a bearing return function model based on deep learning, and a Jacobian matrix of the flight robotic arm based on image moment and vector product methods.Through the experimental research on TPC of the flight manipulator, it was proved that the DL trajectory planning method could reduce the collision risk of the flight manipulator by 4.79% compared with the traditional trajectory planning method, and could improve the task completion speed of the flight manipulator by 4.66%.The application of DL to the TPC of the flight manipulator could improve the trajectory planning effect of the flight manipulator.
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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) | 0.001 | 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 | 0.000 |
| 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".