Talks and Poster Presentations (with Proceedings-Entry):
T. Tuli, L. Kohl, S. Chala, M. Manns, F. Ansari:
"Knowledge-Based Digital Twin for Predicting Interactions in Human-Robot Collaboration";
Talk: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ),
Schweden;
2021-09-07
- 2021-09-10; in: "Proceedings of the 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)",
IEEE,
(2021),
ISBN: 978-1-7281-2989-1.
English abstract:
Semantic representation of motions in a human-robot collaborative environment is essential for agile design and development of digital twins (DT) towards ensuring efficient collaboration between humans and robots in hybrid work systems, e.g., in assembly operations. Dividing activities into actions helps to further conceptualize motion models for predicting what a human intends to do in a hybrid work system. However, it is not straightforward to identify human intentions in collaborative operations for robots to understand and collaborate. This paper presents a concept for semantic representation of human actions and intention prediction using a flexible task ontology interface in the semantic data hub stored in a domain knowledge base. This semantic data hub enables the construction of a DT with corresponding reasoning and simulation algorithms. Furthermore, a knowledge-based DT concept is used to analyze and verify the presented use-case of Human-Robot Collaboration in assembly operations. The preliminary evaluation showed a promising reduction of time for assembly tasks, which identifies the potential to i) improve efficiency reflected by reducing costs and errors and ultimately ii) assist human workers in improving decision making. Thus the contribution of the current work involves a marriage of machine learning, robotics, and ontology engineering into DT to improve human-robot interaction and productivity in a collaborative production environment.
Keywords:
human-robot interaction, digital twin, human action models, ontology, machine learning
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ETFA45728.2021.9613342
Created from the Publication Database of the Vienna University of Technology.