Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9798350339918 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 - Sinaia, Rumänien Dauer: 12 Sept. 2023 → 15 Sept. 2023 |
Publikationsreihe
| Name | IEEE International Conference on Emerging Technologies and Factory Automation, ETFA |
|---|---|
| Band | 2023-September |
| ISSN (Print) | 1946-0740 |
| ISSN (elektronisch) | 1946-0759 |
Abstract
Multi-Robot systems in automotive are safety-critical systems that consist of collaborating-aware robots and components that interact with external components, the environment, or humans at run-time. This implies a significant complexity for the system engineer to design, model, validate the system, and optimize the cycle time, including considering unexpected events at run-time. This paper addresses this challenge by describing a model-driven engineering approach that formally designs the system under the consideration of uncertainties and at run-time optimizes the system actions using learning-based approaches. We implemented this approach in an industrial-inspired case study of a spot-welding multi-robot cell. Based on the system requirements, we generate valid system strategies that consider unexpected events such as robot interruptions and failures. Considering movement and interruption time models, we implemented a reinforcement learning method to optimize system actions at run-time. We show that via simulations and learning, our approach can be used to synthesize time-efficient schedules for robot task assignments that improve the overall cycle time.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Informatik (insg.)
- Angewandte Informatik
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- BibTex
- RIS
Proceedings 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA; Band 2023-September).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - MDE and Learning for flexible Planning and optimized Execution of Multi-Robot Choreographies
AU - Wete, Eric
AU - Greenyer, Joel
AU - Wortmann, Andreas
AU - Kudenko, Daniel
AU - Nejdl, Wolfgang
PY - 2023
Y1 - 2023
N2 - Multi-Robot systems in automotive are safety-critical systems that consist of collaborating-aware robots and components that interact with external components, the environment, or humans at run-time. This implies a significant complexity for the system engineer to design, model, validate the system, and optimize the cycle time, including considering unexpected events at run-time. This paper addresses this challenge by describing a model-driven engineering approach that formally designs the system under the consideration of uncertainties and at run-time optimizes the system actions using learning-based approaches. We implemented this approach in an industrial-inspired case study of a spot-welding multi-robot cell. Based on the system requirements, we generate valid system strategies that consider unexpected events such as robot interruptions and failures. Considering movement and interruption time models, we implemented a reinforcement learning method to optimize system actions at run-time. We show that via simulations and learning, our approach can be used to synthesize time-efficient schedules for robot task assignments that improve the overall cycle time.
AB - Multi-Robot systems in automotive are safety-critical systems that consist of collaborating-aware robots and components that interact with external components, the environment, or humans at run-time. This implies a significant complexity for the system engineer to design, model, validate the system, and optimize the cycle time, including considering unexpected events at run-time. This paper addresses this challenge by describing a model-driven engineering approach that formally designs the system under the consideration of uncertainties and at run-time optimizes the system actions using learning-based approaches. We implemented this approach in an industrial-inspired case study of a spot-welding multi-robot cell. Based on the system requirements, we generate valid system strategies that consider unexpected events such as robot interruptions and failures. Considering movement and interruption time models, we implemented a reinforcement learning method to optimize system actions at run-time. We show that via simulations and learning, our approach can be used to synthesize time-efficient schedules for robot task assignments that improve the overall cycle time.
KW - Choreography planning
KW - Learning-based scheduling under uncertainty
KW - Motion planning
KW - Task planning
UR - http://www.scopus.com/inward/record.url?scp=85175445410&partnerID=8YFLogxK
U2 - 10.1109/ETFA54631.2023.10275559
DO - 10.1109/ETFA54631.2023.10275559
M3 - Conference contribution
AN - SCOPUS:85175445410
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
BT - Proceedings 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023
Y2 - 12 September 2023 through 15 September 2023
ER -