Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 380-385 |
| Seitenumfang | 6 |
| Fachzeitschrift | IFAC-PapersOnLine |
| Jahrgang | 58 |
| Ausgabenummer | 24 |
| Frühes Online-Datum | 19 Nov. 2024 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 12th IFAC Symposium on Biological and Medical Systems, BMS 2024 - Villingen-Schwenningen, Deutschland Dauer: 11 Sept. 2024 → 13 Sept. 2024 |
Abstract
Stem cell therapy is a promising approach to treat heart insufficiency and benefits from automated myocardial injection which requires highly precise motion of a robotic manipulator that is equipped with a syringe. This work investigates whether sufficiently precise motion can be achieved by combining a SCARA robot and learning control methods. For this purpose, the method Autonomous Iterative Motion Learning (AI-MOLE) is extended to be applicable to multi-input/multi-output systems. The proposed learning method solves reference tracking tasks in systems with unknown, nonlinear, multi-input/multi-output dynamics by iteratively updating an input trajectory in a plug-and-play fashion and without requiring manual parameter tuning. The proposed learning method is validated in a preliminary simulation study of a simplified SCARA robot that has to perform three desired motions. The results demonstrate that the proposed learning method achieves highly precise reference tracking without requiring any a priori model information or manual parameter tuning in as little as 15 trials per motion. The results further indicate that the combination of a SCARA robot and learning method achieves sufficiently precise motion to potentially enable automatic myocardial injection if similar results can be obtained in a real-world setting.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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in: IFAC-PapersOnLine, Jahrgang 58, Nr. 24, 2024, S. 380-385.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Autonomous Iterative Motion Learning (AI-MOLE) of a SCARA Robot for Automated Myocardial Injection
AU - Meindl, Michael
AU - Mönkemöller, Raphael
AU - Seel, Thomas
N1 - Publisher Copyright: © 2024 The Authors. This is an open access article under the CC BY-NC-ND license.
PY - 2024
Y1 - 2024
N2 - Stem cell therapy is a promising approach to treat heart insufficiency and benefits from automated myocardial injection which requires highly precise motion of a robotic manipulator that is equipped with a syringe. This work investigates whether sufficiently precise motion can be achieved by combining a SCARA robot and learning control methods. For this purpose, the method Autonomous Iterative Motion Learning (AI-MOLE) is extended to be applicable to multi-input/multi-output systems. The proposed learning method solves reference tracking tasks in systems with unknown, nonlinear, multi-input/multi-output dynamics by iteratively updating an input trajectory in a plug-and-play fashion and without requiring manual parameter tuning. The proposed learning method is validated in a preliminary simulation study of a simplified SCARA robot that has to perform three desired motions. The results demonstrate that the proposed learning method achieves highly precise reference tracking without requiring any a priori model information or manual parameter tuning in as little as 15 trials per motion. The results further indicate that the combination of a SCARA robot and learning method achieves sufficiently precise motion to potentially enable automatic myocardial injection if similar results can be obtained in a real-world setting.
AB - Stem cell therapy is a promising approach to treat heart insufficiency and benefits from automated myocardial injection which requires highly precise motion of a robotic manipulator that is equipped with a syringe. This work investigates whether sufficiently precise motion can be achieved by combining a SCARA robot and learning control methods. For this purpose, the method Autonomous Iterative Motion Learning (AI-MOLE) is extended to be applicable to multi-input/multi-output systems. The proposed learning method solves reference tracking tasks in systems with unknown, nonlinear, multi-input/multi-output dynamics by iteratively updating an input trajectory in a plug-and-play fashion and without requiring manual parameter tuning. The proposed learning method is validated in a preliminary simulation study of a simplified SCARA robot that has to perform three desired motions. The results demonstrate that the proposed learning method achieves highly precise reference tracking without requiring any a priori model information or manual parameter tuning in as little as 15 trials per motion. The results further indicate that the combination of a SCARA robot and learning method achieves sufficiently precise motion to potentially enable automatic myocardial injection if similar results can be obtained in a real-world setting.
KW - Autotuning
KW - Intelligent robotics
KW - Iterative learning control
KW - Iterative modeling and control design
KW - Motion Control Systems
UR - http://www.scopus.com/inward/record.url?scp=85210842490&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2409.06361
DO - 10.48550/arXiv.2409.06361
M3 - Conference article
AN - SCOPUS:85210842490
VL - 58
SP - 380
EP - 385
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8971
IS - 24
T2 - 12th IFAC Symposium on Biological and Medical Systems, BMS 2024
Y2 - 11 September 2024 through 13 September 2024
ER -