
Robotics plays a key role in industry, and its use continues to grow. Robots are used in many sectors to increase the efficiency, productivity, and safety of work processes. This manuscript focuses on the spatial calibration of collaborative robot arms using appropriate statistical tools. Nowadays, there are many dedicated programming languages, simulations or virtual reality (VR), which in most cases perform calibration using matrix relations. The mathematical-statistical solution is not often addressed, and the use of linear relationships is valid only in certain parts of the workspace of the collaborative robot. The purpose of this article is to demonstrate how to find a suitable statistical method that would respect the wear of the arm mechanism in predefined positions based on the requirements of ISO 230–2:2015. Based on these measurements, it is possible to assume that optimal solutions can be obtained using a polynomial regression function. This optimization method will be explored using the Newton and Markwartel methods.
Operational Programme Integrated Infrastructure [313011W442]; European Regional Development Fund [313011W442]; European Union [IGA/FT/2024/002, MVP01_2024]; Internal Grant Scheme of the Alexander Dubcek University of Trencin; [09I03-03-V05-00010]
internalgrantofTBU in Zlin; Tomas Bata University in Zlín, TBU, (IGA/FT/2024/002)
000, industrial robots, polynomial regression, error measurement, collaborative robot, kinematic models, 620
000, industrial robots, polynomial regression, error measurement, collaborative robot, kinematic models, 620
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
