An Adaptive Robot Trajectory Planning Method for Measurement of Thin-Walled Workpieces with Variable Curvature
Bibliographic record
Abstract
To address the intelligent detection requirements during the roll-bending process of large aerospace thin-walled workpieces, this paper proposes a robot trajectory planning method for measurement that incorporates dynamic curvature characteristics, aiming to enhance the precision of laser-based inspection. Firstly, an intelligent measurement system is constructed to analyze the kinematic relationships among the thin-walled workpiece, the industrial robot, and the laser camera. A unified coordinate system is established through spatial coordinate transformation. Next, an adaptive sampling strategy is designed based on the curvature distribution of the workpiece, where dynamic curvature thresholds segment the surface cross-sectional profiles. Sampling points are dynamically generated within each subregion according to the laser camera's field of view. Subsequently, Principal Component Analysis (PCA) is employed to calculate surface normal vectors, and these sampling points are transformed into robot trajectory points. To ensure motion smoothness and stability, S-curve algorithm is implemented for joint trajectory planning. Experimental results demonstrate that the proposed method adaptively generates robot trajectories by integrating surface characteristics of aerospace thin-walled workpieces, achieving improvement in measurement accuracy compared to other sampling methods while maintaining robot motion stability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".