Assessment of the positioning performance of an industrial robot
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the use of a laser tracker, a laser interferometer system and a telescopic ballbar for assessing the positioning performance of a six‐axis industrial serial robot. The paper also aims to illustrate the limitations of these three metrology instruments for the assessment of robot positioning performance and to demonstrate the inadequacy of simplistic performance tests. Design/methodology/approach Specific test methods in the case of the laser interferometer system and the telescopic ballbar are proposed. Measurements are analyzed in accordance to the ISO 9283 norm. Findings It is found that, in static conditions and after a relatively short warm‐up, the unidirectional position repeatability of the non‐calibrated industrial robot under study (an ABB IRB 1600) is better than 37 μm, the unidirectional orientation repeatability is at worst 87 μrad, the linear position accuracy is better than 650 μm, and the rotation accuracy is at worst 2.8 mrad (mainly because of the sixth robot axis). It was also found that the dynamic (radial) errors due to vibrations can be up to approximately ±250 μm along a small circular path at TCP speed of 700 mm/s. Practical implications It is pointed out that the use of a laser tracker (or any other large range portable 3D measurement system) is questionable for assessing – let alone analyzing in depth – the unidirectional position repeatability of some of today's industrial robots. It is also demonstrated that the laser interferometer system can be used for measuring linear errors along a linear path of motion as well as angular errors about axes orthogonal to the path of motion. Finally, it is shown that the telescopic ballbar is an excellent, comparably low‐cost, high‐precision tool for assessing the static and dynamic positioning performance of industrial robots and its use in robotics should be further developed. Originality/value This work is the first to detail the use of three metrology equipments for assessing the positioning performance of an industrial robot. Experimental results are presented and discussed. Some guidelines for optimizing the positioning performance of an industrial robot are provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".