Tool Center Point Calibration Method for an Industrial Robots based on Spheres Fitting Method
Bibliographic record
Abstract
As robots become more sophisticated, they can handle an increasing number of industrial processes. Six-degree-of-freedom (6DoF) robots are capable of complex motions that allow them to perform many complicated industrial tasks such as palletizing, handling, gluing, and welding using a wide variety of tools. However, to take advantage of the capabilities of these robots, accurate recalibration is required each time a new tool is introduced. There are various methods for calibrating the tool center point (TCP). Contact with reference parts, the use of distance sensors, and the use of laser interferometry are just a few. These methods show acceptable results for some industrial applications with maximum position error in the range of 2mm and 0.8mm. External sensors, such as camera systems, can also be attached to different locations on the robot to acquire the precise position of a reference object from which the robot can be calibrated. These non-contact calibration methods are more accurate and efficient than the traditional multi-point contact method, but the complex system components and high cost limit their application. The goal of this work is to improve accuracy of results given by ‘four-point calibration’ method provided by the industrial robot. A sphere fitting calibration approach based on the four-point calibration method findings as initial data is proposed in this study to avoid the exact point-to-point matching operation in the low-cost multi-point calibration method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".