Complete Coverage Path Planning Algorithm Based on Improved Biologically Inspired Neural Networks in Spray Painting
Bibliographic record
Abstract
Intelligent putty coating technology is the main way to improve the degree of automation of railroad vehicle painting workshops. The two-component putty, which is currently used in the vehicle coating system, has extremely low fluidity, which requires improving the full coverage of the spray path while minimizing the number of gun switches, i.e., reducing the path overlap rate. We propose the following improvements for the complete coverage path planning (CCPP) of putty spraying robots as well as the under-performance of path planning by biologically inspired neural networks (BINN) algorithms: (1) making full use of the topological neurons of the traversed area to establish the feedback adjustment coefficients, and (2) optimizing the steering parameter terms in the movement control equation to solve the optimal path point selection problem. To minimize the path repetition rate and reduce the probability of dead zones, a region decomposition detection algorithm and an avoidance mechanism are proposed. Many simulation results show that the proposed improved algorithm is better adapted, and the generated spraying paths are coherent and orderly with a low repetition rate, which can meet the spraying requirements of the existing rail vehicles.
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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 | 0.000 | 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".