An Adaptive Robot Trajectory Planning Method for Measurement of Thin-Walled Workpieces with Variable Curvature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it