Robust position-based visual servoing of industrial robots using feedforward kinematic approach based on integral quasi-super twisting algorithm
Bibliographic record
Abstract
This paper presents a novel robust kinematic control approach based on feedforward inverse kinematic compensation for real-time pose correction of vision-based industrial robots to enhance the trajectory tracking accuracy. The conventional methods entail the robust design of the dynamic controller to handle the uncertainties in the dynamical model, which is not applicable to most industrial robots since they operate based on their built-in controller designed by the manufacturer and usually are not accessible. The proposed method, however, is a robust kinematic controller capable of handling the uncertainties in both dynamic and kinematic models. To this end, first, the robot's pose is estimated by a nonphysical contact sensor (in this research, a photogrammetry sensor). Then it is fed to the kinematic controller for the real-time pose control task. The feedforward part of the proposed controller, which is the inverse kinematic function of the robot, is considered in the control system design to reach the highest possible accuracy, especially for trajectory tracking purposes. The proposed method utilizes a novel integral quasi-super twisting algorithm (IQSTA) as the compensator within the control loop to reach a finite-time convergence with very high precision and robust performance with minimal chattering. The stability analysis of the proposed method is presented. The experimental results on an industrial robot (Denso VP-6242) equipped with an ATEMEK photogrammetry sensor show the superiority of the proposed method over other state-of-the-art approaches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".