Visual Sensor-Based Dynamic Identification of a 6-RSS Parallel Robot
Bibliographic record
Abstract
The parallel robot, also known as parallel kinematic machine (PKM), has some unique properties such as higher speed, stiffness, and load carrying capacity compared with serial robots. However, the dynamics of the PKM is normally more complex than that of serial robots, due to the highly coupling relation between the moving components. Hence this property affects the control performance of the PKM. To address this issue, the dynamic identification of the PKM is discussed for dynamic visual servoing in this paper. A visual sensor-based dynamic identification method for a 6-DOF revolute-sphere-sphere (6-RSS) PKM is proposed. In contrast to the classic method for PKMs, the proposed method doesn't require the actuator torque measurement and the parameters of the internal robot controller. The forward kinematics of PKMs are not needed. In this paper, the principle of virtual work method is utilized to build the dynamic model of the 6-RSS PKM. A visual sensor based closed-loop identification algorithm is developed to estimate the dynamic parameters of the PKM. The experiment tests show that the output of the identified model matches that of the PKM testbed with satisfactory accuracy when both systems are subjected to the same desired path.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".