A Hybrid Optimization Scheme for Efficient Trajectory Planning of a Spray-Painting Robot
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
This paper presents a novel hybrid optimization scheme for generating time and energy efficient paint trajectories while ensuring optimal coating deposition over complex free-form surfaces. By utilizing a 3D sensor, the geometric model of the object is obtained in the form of a point cloud. Then, using principal component analysis, it is transformed into its eigen coordinate frame followed by slicing at an arbitrary direction. Using a double beta distribution model, the coating thickness function between two consecutive slicing planes is formulated. To compute robot energy, the recursive Newton Euler approach is used following the definition of the robot kinematic and dynamic model. Finally, using a genetic algorithm, coating uniformity, process time, and energy objective functions are minimized to obtain the optimal slicing direction, width, speeds, and the inverse kinematic configuration of the robot. Results for the trajectory planning of a car door, hood, and bumper reveal efficient paint trajectories can be obtained using the proposed optimization scheme.
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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 | 0.000 | 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 | 0.000 |
| 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