Details
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 385-392 |
| Number of pages | 8 |
| ISBN (electronic) | 9781538637418 |
| Publication status | Published - 23 Mar 2018 |
| Event | 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 - Macau, China Duration: 5 Dec 2017 → 8 Dec 2017 |
Abstract
Advances in the rapidly growing field of soft robotics show that robotic systems and devices made from soft materials surpass rigid-links robots in terms of adaptability and flexibility. As such, soft robots are believed to bridge the gap between humans and autonomous machines. Despite an increasing sophistication in the development of soft robots, research on closed loop control of soft robots still lags behind. This can be partly attributed to the high nonlinearities, which complicate accurate modeling. Artificial neural networks (ANN) can be a very powerful tool for capturing even those non-linearities that are very often neglected. In this article, we extend our previous research on finite element (FE) based training of artificial neural networks for modular soft robots. We present a method by which sufficiently large amounts of training data can be generated in order to learn the kinematic model of a soft pneumatic actuator which can move in three-dimensional space. The method is generic and can be employed for learning of kinematic models for simulation and control.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
- Mathematics(all)
- Modelling and Simulation
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2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 385-392.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - FEM-based training of artificial neural networks for modular soft robots
AU - Runge-Borchert, Gundula
AU - Wiese, Mats
AU - Raatz, Annika
PY - 2018/3/23
Y1 - 2018/3/23
N2 - Advances in the rapidly growing field of soft robotics show that robotic systems and devices made from soft materials surpass rigid-links robots in terms of adaptability and flexibility. As such, soft robots are believed to bridge the gap between humans and autonomous machines. Despite an increasing sophistication in the development of soft robots, research on closed loop control of soft robots still lags behind. This can be partly attributed to the high nonlinearities, which complicate accurate modeling. Artificial neural networks (ANN) can be a very powerful tool for capturing even those non-linearities that are very often neglected. In this article, we extend our previous research on finite element (FE) based training of artificial neural networks for modular soft robots. We present a method by which sufficiently large amounts of training data can be generated in order to learn the kinematic model of a soft pneumatic actuator which can move in three-dimensional space. The method is generic and can be employed for learning of kinematic models for simulation and control.
AB - Advances in the rapidly growing field of soft robotics show that robotic systems and devices made from soft materials surpass rigid-links robots in terms of adaptability and flexibility. As such, soft robots are believed to bridge the gap between humans and autonomous machines. Despite an increasing sophistication in the development of soft robots, research on closed loop control of soft robots still lags behind. This can be partly attributed to the high nonlinearities, which complicate accurate modeling. Artificial neural networks (ANN) can be a very powerful tool for capturing even those non-linearities that are very often neglected. In this article, we extend our previous research on finite element (FE) based training of artificial neural networks for modular soft robots. We present a method by which sufficiently large amounts of training data can be generated in order to learn the kinematic model of a soft pneumatic actuator which can move in three-dimensional space. The method is generic and can be employed for learning of kinematic models for simulation and control.
UR - http://www.scopus.com/inward/record.url?scp=85049925012&partnerID=8YFLogxK
U2 - 10.1109/robio.2017.8324448
DO - 10.1109/robio.2017.8324448
M3 - Conference contribution
AN - SCOPUS:85049925012
SP - 385
EP - 392
BT - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Y2 - 5 December 2017 through 8 December 2017
ER -