Dynamic Path Correction of an Industrial Robot Using a Distance Sensor and an ADRC Controller
Bibliographic record
Abstract
Commercially available six-axis industrial robots, though highly repeatable, have relatively low accuracy. While robot calibration can improve pose accuracy, the only way for a user to improve path accuracy is by “guiding” the robot with the help of an external sensor and a control algorithm running on a separate computer. For this purpose, industrial robots, which are normally controlled with preprogrammed position-mode instructions, sometimes offer the possibility to modify the pose of the robot end-effector on the fly. In the case of Mecademic's Meca500 robot,1users can indirectly modify the end-effector pose by controlling the robot joint or Cartesian velocity. In this article, a practical application of an active disturbance rejection control scheme is presented to improve the path accuracy of the Meca500. The dynamic path correction is achieved by first measuring the distance between a fixed point and the robot tooltip with a linear transducer (Renishaw's QC20-W ballbar), and then feeding the tooltip velocity vector to the robot (via Ethernet TCP/IP). The (circular) path accuracy of the robot is significantly improved for different robot TCP velocities. For example, at 50 mm/s, the maximum radial error is less than 0.100 mm, and the mean error is 0.015 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".