Details
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Pages | 12405-12411 |
| Number of pages | 7 |
| ISBN (electronic) | 978-1-6654-7927-1 |
| Publication status | Published - 2022 |
| Externally published | Yes |
| Event | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022 |
Publication series
| Name | Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems |
|---|---|
| ISSN (Print) | 2153-0858 |
| ISSN (electronic) | 2153-0866 |
Abstract
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
Cite this
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- BibTeX
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2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2022. p. 12405-12411 ( Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Autonomous Cycle Time Reduction of Robotic Tasks Using Iterative Learning Control
AU - Halt, Lorenz
AU - Meindl, Michael
AU - Bayer, Victor
AU - Kraus, Werner
AU - Seel, Thomas
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - When robots are used to automate repetitive production tasks, the productivity of the manufacturing system crucially depends on the robot's task execution speed. An out-of-the-box solution is typically slow, whereas achieving shorter cycle times typically requires large efforts with respect to controller design and tuning. This dilemma can be resolved by learning control algorithms that autonomously improve performance without requiring any system-specific tuning. In the present work, we propose a novel learning control scheme that autonomously reduces the execution times of robotic systems that perform repetitive manufacturing tasks. To this end, we combine an Iterative Learning Control (ILC) approach with a trial-varying reference adaptation. The reference trajectory is slowly adapted to ensure that the given task is performed successfully on every single iteration without constraint violations. Therefore, the learning process can be carried out during operation. We validate the practical applicability of the method by real-world experiments on a 6-axis robot that performs a linear motion and a contact-force task. Despite the fundamentally different characteristics of these two tasks, the proposed algorithm achieves a remarkable reduction of cycle times, namely, by a factor of 4 in the linear motion task and a factor of 10 in the contact-force task. These results provide an important step toward robotic manufacturing systems that autonomously optimize their own performance during operation.
AB - When robots are used to automate repetitive production tasks, the productivity of the manufacturing system crucially depends on the robot's task execution speed. An out-of-the-box solution is typically slow, whereas achieving shorter cycle times typically requires large efforts with respect to controller design and tuning. This dilemma can be resolved by learning control algorithms that autonomously improve performance without requiring any system-specific tuning. In the present work, we propose a novel learning control scheme that autonomously reduces the execution times of robotic systems that perform repetitive manufacturing tasks. To this end, we combine an Iterative Learning Control (ILC) approach with a trial-varying reference adaptation. The reference trajectory is slowly adapted to ensure that the given task is performed successfully on every single iteration without constraint violations. Therefore, the learning process can be carried out during operation. We validate the practical applicability of the method by real-world experiments on a 6-axis robot that performs a linear motion and a contact-force task. Despite the fundamentally different characteristics of these two tasks, the proposed algorithm achieves a remarkable reduction of cycle times, namely, by a factor of 4 in the linear motion task and a factor of 10 in the contact-force task. These results provide an important step toward robotic manufacturing systems that autonomously optimize their own performance during operation.
UR - http://www.scopus.com/inward/record.url?scp=105004652899&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9981042
DO - 10.1109/IROS47612.2022.9981042
M3 - Conference contribution
SN - 978-1-6654-7928-8
T3 - Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
SP - 12405
EP - 12411
BT - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Y2 - 23 October 2022 through 27 October 2022
ER -