Details
| Original language | English |
|---|---|
| Pages (from-to) | 380-385 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 58 |
| Issue number | 24 |
| Early online date | 19 Nov 2024 |
| Publication status | Published - 2024 |
| Event | 12th IFAC Symposium on Biological and Medical Systems, BMS 2024 - Villingen-Schwenningen, Germany Duration: 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.
Keywords
- Autotuning, Intelligent robotics, Iterative learning control, Iterative modeling and control design, Motion Control Systems
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IFAC-PapersOnLine, Vol. 58, No. 24, 2024, p. 380-385.
Research output: Contribution to journal › Conference article › Research › 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 -