Improved Seven-Segment Acceleration/Deceleration Algorithms Based on Cosine and Exponential Functions for Vibration Suppression of Multi-Axis Robotic Arms
Bibliographic record
Abstract
As core equipment in intelligent manufacturing, residual vibration of robotic arms during high-speed motion directly affects positioning accuracy and service life. Focusing on vibration suppression in multi-axis robotic arms, this paper addresses the limitations of the traditional seven-segment S-curve (7S) acceleration/deceleration algorithm in jerk continuity and vibration control. Two improved seven-segment algorithms based on cosine and exponential functions are proposed to enhance vibration suppression performance by smoothing jerk transitions. Mathematical models of the traditional 7S, seven-segment cosine (7S-Cos), and seven-segment exponential (7S-Exp) algorithms are established. Experiments are conducted under practical operating conditions with a 4 kg load at full speed, using joint synchronization control to evaluate end-effector residual vibration. Results show that the 7S-Cos algorithm achieves the smallest vibration amplitude and fastest attenuation, significantly outperforming the traditional 7S and 7S-Exp algorithms while maintaining motion efficiency. The proposed method provides a practical solution for vibration-sensitive robotic applications such as precision assembly and high-speed handling.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".