Optimal Experiment Design for Elasto-Geometrical Calibration of Industrial Robots
Why this work is in the frame
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Bibliographic record
Abstract
Inaccuracy of the kinematic model used in robot controllers and deflection of robot joints are two main sources of positioning errors in current industrial robots. We propose an elasto-geometrical calibration method to address these problems. The elasto-geometrical calibration identifies the accurate kinematic model and joint elasticities of any industrial serial robot by measuring the robot tool position at multiple design points. Each design point indicates a unique combination of a robot configuration (set of joint values) and an external load on the robot tool. Proper selection of the design points could significantly improve the calibration accuracy and reduce the experiment time. We propose an optimal design of experiment to find the D-, A-, and E-optimal designs from a large pool of candidate design points. Unlike the existing approaches, we use a semidefinite convex programming that can find a suboptimal design of experiments. The efficiency of the proposed elasto-geometrical calibration is evaluated on an ABB IRB 1600 robot. For this experiment, a cable-driven parallel robot is employed to apply multidirectional external loads on the tool of the ABB robot. Experimental results show that the proposed calibration method significantly improves the robot's accuracy in comparison with a regular kinematic calibration method. In addition, the D-optimal design results in less positioning error than A- and E-optimal designs.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it