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  4. RTMN 2.0: An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration
 

RTMN 2.0: An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration

URI
https://arbor.bfh.ch/handle/arbor/36733
Version
Published
Date Issued
2023
Author(s)
Zhang Sprenger, Congyu  
Corrales Ramón, Juan Antonio
Baier, Norman Urs  
Type
Article
Language
English
Subjects

RTMN

HRC

HRC mode

HRC task

KPI

requirement

decision making

Abstract
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.
Subjects
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
DOI
10.24451/arbor.21007
https://doi.org/10.24451/arbor.21007
Publisher DOI
10.3390/app14010283
Journal
Applied Sciences
ISSN
2076-3417
Publisher URL
https://www.mdpi.com/2076-3417/14/1/283
Organization
Intelligente industrielle Systeme (I3S)  
I3S / Prozessoptimierung in der Fertigung  
Technik und Informatk  
Volume
14
Issue
1
Publisher
MDPI
Submitter
Baier, Norman Urs
Citation apa
Zhang Sprenger, C., Corrales Ramón, J. A., & Baier, N. U. (2023). RTMN 2.0: An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration. In Applied Sciences (Vol. 14, Issue 1). MDPI. https://doi.org/10.24451/arbor.21007
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applsci-14-00283-v2-3.pdf

License
Attribution 4.0 International
Version
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Size

8.63 MB

Format

Adobe PDF

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