This article presents a solution to support the identification of important user coordinate systems for creating an industrial robot work programme. The objective of this article is to provide a support tool for users for a task that may arise numerous times during the lifecycle of industrial robots. The solution described in the article offers a developable support tool that can be transferred from the industrial robotics environment to the collaborative robots' application environment, where the proportion of professionally trained and experienced users may be smaller compared to the previously mentioned area. By reducing the time required and improving the quality of task results, it can contribute to increasing the efficiency of robot systems.

Nowadays, the application of industrial robots in manufacturing is widespread to automatize tedious and repeatable precision-sensitive processes. Modern robotic cells compete with the human workforce and specialized industrial machines in general efficiency. Additionally, robotic cells can be typically reprogrammed for different tasks, thus providing flexible applications, especially compared to more traditional methods. Advanced autonomous and intelligent capabilities are widespread in modern robotic systems, supplying quantitative factors like higher availability, fault tolerance, precision, and system flexibility. Robotic systems typically work measurably more efficiently regarding resource usage, electrical power, and waste than other industrial production line systems. New intelligent and interconnected methods (e.g., cooperative robotics, big data analysis, cyber-physical systems) contribute further to the operation efficiency. Recently, sustainability has become a significant question in robotics: modern technical society faces problems such as decreasing availability of natural resources, workforce scarcity, and environmental challenges. This work focuses on industrial robotic systems as a primary pillar for sustainability efforts in numerous production sectors, such as metallurgy, assembly lines, and construction. This paper aims to review and analyze possible sustainability indicators of the industrial applications of robotics. Based on the analysis, the paper presents a manufacturing process model with industrial robots that considers sustainability indicators. This model highlights the relationship between industrial robotization and sustainability from a quantitative and qualitative perspective. The model indicates that energy efficiency and data interconnectivity play a crucial role in the sustainability of industrial processes. The aim of the model is to help identify indicators playing a role in the sustainability of industrial robot-based manufacturing lifecycle and maintenance.

Industrial robots generate diverse types of data, such as position, orientation, coordinate system definitions, tooling information, and safety zone configurations. Handling these heterogeneous datasets uniformly is challenging, especially when integrating data from multiple robot manufacturers within augmented reality (AR) environments. This research introduces a unified data structure that encapsulates robot-specific data along with supplementary metadata, including source identifiers, unique data identifiers, data types, original orientation formats, status data, and timestamps. The developed data model, validated by JSON Schema, ensures consistent interpretation and ease of integration into AR visualization environments.