Suppression of robot vibrations using input shaping and learning-based structural models
Bibliographic record
Abstract
Industrial robots used in manufacturing processes such as drilling of aerospace structures undergo many rapid positioning motions during each operation. Such aggressive motions excite the structural modes of the robot and cause inertial vibrations at the end-effector, which may damage the part and violate the tolerance requirements. This article presents a vibration avoidance technique based on input shaping combined with a learning-based structural dynamic model. A theoretical dynamic model is first developed for commonly used robotic arms considering the flexibilities of the first three joints of the robot. An artificial neural network is developed and used in conjunction with the dynamic model to predict the natural frequency of the system at any pose in the workspace. Transfer learning techniques are used to extend the trained artificial neural network to account for the mass of the payload with minimal data collection. To reduce the residual vibrations of the robot in rapid motions, zero-vibration derivative shapers are designed and implemented. The effectiveness of the presented methodology has been validated experimentally on a Staubli RX90CR robot with an open-architecture control system developed fully in-house. Experimental results show more than 85% reduction in residual vibrations during aggressive motions of the robot.
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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 | 0.000 | 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) | 0.001 | 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".