Elasto-geometrical calibration of an industrial robot under multidirectional external loads using a laser tracker
Bibliographic record
Abstract
This paper presents an elasto-geometrical calibration method for improving the position accuracy of an industrial robot (ABB IRB 1600). Geometric parameter errors and joint stiffness parameters are identified through measuring the position of the robot's end-effector in several robot configurations using a laser tracker. Contrary to previous works, robot's position errors are measured under a wide range of external forces and torques for each robot configuration. A 6-DOF cable-driven parallel robot is employed to automatically apply the desired load on the end-effector of the ABB robot. Before the experiment, an observability analysis is performed in order to improve the robustness of the calibration process with respect to measurement noise and unmodeled errors. Accordingly, an optimal set of robot configurations and external loads is selected for the calibration process. The measured position errors of the ABB robot for this selected set are used to identify the real robot's elasto-geometrical parameters. Finally, the calibration efficiency is evaluated for a number of random combinations of robot configurations and external loads. The experimental results revealed that the proposed elasto-geometrical calibration approach is able to reduce the maximum position error to 0.960 mm, while a customary kinematic calibration can reduce the maximum position error only to 2.571 mm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".