An improved robot trajectory planning method considering torque saturation and modeling errors
Bibliographic record
Abstract
Time-optimal trajectory planning algorithms have been widely adopted to minimize the motion time by exploiting the dynamics and joint allowable torques of a robotic manipulator. However, the actual joint torques may exceed the joint allowable torques because of modelling errors or disturbances in the control system. When the torque limit is added for actuator safety, the controller will have no margin to deal with modeling errors or disturbances, which may lead to large path tracking errors. An on-line trajectory time scaling method called path velocity controller can improve path tracking performance by modifying the path velocity when torque saturation occurs. However, the path velocity controller is based on a feedforward or computed torque controller, so the dynamic modelling errors will worsen the path tracking performance. In addition, the motion time may also be increased because the dynamic modelling errors could result in longer duration time of torque saturation. To further improve the path tracking performance of a path velocity controller, a path velocity controller with an on-line parameter estimate mechanism is proposed. The simulation results show that the proposed method can achieve a better path tracking performance and shorter motion time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".