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  4. Decentralized process learning for cyber-physical systems

Decentralized process learning for cyber-physical systems

Publikationstyp
Conference Paper
Date Issued
2026-03
Sprache
English
Author(s)
Tran, Trinh
Networked Cyber-Physical Systems E-17
Landsiedel, Olaf
Networked Cyber-Physical Systems E-17
TORE-URI
https://hdl.handle.net/11420/64244
First published in
GI-Edition
Number in series
P-380
Start Page
19
End Page
20
Citation
16th International Workshop on Enterprise Modeling and Information Systems Architecture, EMISA 2026
Contribution to Conference
16th International Workshop on Enterprise Modeling and Information Systems Architecture, EMISA 2026
Publisher DOI
10.18420/EMISA2026_04
Scopus ID
2-s2.0-105045774699
Publisher
Gesellschaft für Informatik e.V. (GI)
The application of robots in industries is increasing, and the complexity of modern robotic systems demands systematic and well-structured planning. There is a growing need for design approaches that provide both structure and the flexibility to explore alternative solutions early in the requirements and system design phases. Model-Based Systems Engineering offers such an approach by enabling the specification, analysis, and design of complex systems through the use of system models. However, current robot design approaches rarely consider kinematic properties during the requirements phase, which hinders evaluating whether the robot’s realistic capabilities that depend on the robot's kinematic capabilities align with the task requirements early in the design process. In this paper, we propose the use of system models for the specification of robot kinematic properties and demonstrate how these models can be used to evaluate robot capabilities against their requirements.
Subjects
Robot
Capabilities
Requirements
Goal Model
Kinematics
SysML
DDC Class
600: Technology
Funding(s)
United Nations University Hub on Engineering to Face Climate Change at Hamburg University of Technology
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