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

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 g...

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Bibliographic Details
Published in2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) pp. 15 - 20
Main Authors Sprenger, Congyu Zhang, Ribeaud, Thomas
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.12.2022
Subjects
Bibliographies
Bridges
Industries
Intelligent automation
ontology
Production
robot skill
robot task
RTM
Service robots
Task analysis
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Summary: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 ontologyenabled 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.
DOI:10.1109/RAAI56146.2022.10092996