Robotic Process Automation with Ontology-enabled Skill-based Robot Task Model and Notation (RTMN)

Zhang Sprenger, Congyu; Ribeaud, Thomas (11 December 2022). Robotic Process Automation with Ontology-enabled Skill-based Robot Task Model and Notation (RTMN) In: 2nd IEEE International Conference on Robotics, Automation and Artificial Intelligence (RAAI 2022). Singapur. Dec 9, 2022 - Dec 11, 2022.

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Non-robotic experts are facing challenges in the fast-growing agile production industry. On the one hand robot programming is time consuming and costly and requires high levels of expertise. On the other hand, current systems are difficult understand and control. The authors propose to bridge this gap by introducing an intuitive way of modeling and programming robotic processes that enables nonexperts to plan and program robot tasks. The authors conducted a literature review, and then adopted both quantitative and qualitative methods in the project ACROBA to deepen the research in this topic. The authors propose a model-driven framework that combines modeling and programming in a graphical way using RTMN - an ontology-enabled skill-based robot task model and notation. Results from the validation process indicate that users find RTMN notations simple to understand and intuitive to use.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	School of Engineering and Computer Science > Intelligente industrielle Systeme (I3S) > I3S / Prozessoptimierung in der Fertigung School of Engineering and Computer Science
Name:	Zhang Sprenger, Congyu0000-0003-4652-4857 and Ribeaud, Thomas
Subjects:	T Technology > TS Manufactures
Funders:	[UNSPECIFIED] Horizon 2020
Projects:	[UNSPECIFIED] ACROBA
Language:	English
Submitter:	Norman Urs Baier
Date Deposited:	01 Feb 2023 13:36
Last Modified:	01 Feb 2023 13:36
Uncontrolled Keywords:	Robotics, Modeling Language, Manufacturing
ARBOR DOI:	10.24451/arbor.18794
URI:	https://arbor.bfh.ch/id/eprint/18794