This paper describes RTMN 2.0, an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. Intuitive robot programming allows those without programming expertise to plan and control robots through easily understandable predefined modeling notations. These notations achieve no-code programming and serve as templates for users to create their processes via drag-and-drop functions with graphical representations. The design of the graphical user interface is based on a user survey and gaps identified in the literature We validate our survey through the most influential technology acceptance models, with two major factors: the perceived ease of use and perceived usefulness. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modeling language, RTMN 2.0 concentrates on human–robot collaboration (HRC), which represents the current trend of the industry shift from “mass-production” to “mass-customization”. The biggest contribution that RTMN 2.0 makes is the creation of synergy between HRC modes (based on ISO standards) and HRC task types in the literature. They are modeled as five different HRC task notations: Coexistence Fence, Sequential Cooperation SMS, Teaching HG, Parallel Cooperation SSM, and Collaboration PFL. Both collaboration and safety criteria are defined for each notation. While traditional isolated robot systems in “mass-production” environments provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity in order to be adapted to the variability of customized products. Therefore, human–robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in the new “mass-customization” industrial environments. HRC has made a great impact on the robotic industry: it leads to increased efficiency, reduced costs, and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension of RTMN 2.0 includes the following notations: HRC tasks, requirements, Key Performance Indicators (KPIs), condition checks and decision making, join/split, and data association. With these additional elements, RTMN 2.0 meets the full range of criteria for agile manufacturing—light-out manufacturing is a manufacturing philosophy that does not rely on human labor.

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.