TY - GEN
T1 - Compensating for Material Deformation in Foldable Robots via Deep Learning - A Case Study
AU - Sharifzadeh, Mohammad
AU - Jiang, Yuhao
AU - Lafmejani, Amir Salimi
AU - Aukes, Daniel M.
N1 - Funding Information:
1Mohammad Sharifzadeh and Daniel Aukes are with the Polytechnic School, Fulton Schools of Engineering, Arizona State University, Mesa, AZ, 85212, USA sharifzadeh@asu.edu;danaukes@asu.edu 2Yuhao Jiang is with School for Engineering of Matter, Transport and Energy, Fulton Schools of Engineering, Arizona State University, Tempe, AZ, 85281, USA yuhao92@asu.edu 2Amir Salimi Lafmejani is with School of Electrical, Computer and Energy Engineering, Fulton Schools of Engineering, Arizona State University, Tempe, AZ, 85281, USA asalimil@asu.edu ∗These authors contributed equally to the paper. (Corresponding author: Daniel M. Aukes) This work is partially supported by the National Science Foundation Grant No. 1935324. Supplementary video: https://youtu.be/32UFB4Ziq0I
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Foldable, origami-inspired, and laminate mechanisms are highly susceptible to deformation under external loading, which can lead to position or orientation errors if idealized kinematic models are used. According to dimensional scaling laws, laminate devices can often be treated as rigid bodies at millimeter and smaller scale deformations. However, foldable mechanisms enter the territory of soft robots at larger scales. In this paper, we show the effect of external loads applied to a laminate, 2-DOF parallel robot and the corresponding errors during a pointing task. We then present two control methods, based on deep learning, that compensates for errors caused by the material deformation in foldable robots. For each proposed control method, a Deep Neural Network (DeepNN) is trained to learn the end-effector's deformation model in no-load and loaded conditions. A DeepNN called an updating network is trained and applied in real-time using measured sensor data, in order to transfer updated weights into another DeepNN called the target network. The target network generates control signals with the aim of compensating for the end-effector's error in tracking a desired trajectory. We evaluate our proposed control methods when applied to a laminate robotic end-effector under different external loading conditions in tracking spiral paths. The experimental results show the effectiveness of our proposed control methods in compensating for material deformation in foldable robots.
AB - Foldable, origami-inspired, and laminate mechanisms are highly susceptible to deformation under external loading, which can lead to position or orientation errors if idealized kinematic models are used. According to dimensional scaling laws, laminate devices can often be treated as rigid bodies at millimeter and smaller scale deformations. However, foldable mechanisms enter the territory of soft robots at larger scales. In this paper, we show the effect of external loads applied to a laminate, 2-DOF parallel robot and the corresponding errors during a pointing task. We then present two control methods, based on deep learning, that compensates for errors caused by the material deformation in foldable robots. For each proposed control method, a Deep Neural Network (DeepNN) is trained to learn the end-effector's deformation model in no-load and loaded conditions. A DeepNN called an updating network is trained and applied in real-time using measured sensor data, in order to transfer updated weights into another DeepNN called the target network. The target network generates control signals with the aim of compensating for the end-effector's error in tracking a desired trajectory. We evaluate our proposed control methods when applied to a laminate robotic end-effector under different external loading conditions in tracking spiral paths. The experimental results show the effectiveness of our proposed control methods in compensating for material deformation in foldable robots.
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U2 - 10.1109/ICRA46639.2022.9811752
DO - 10.1109/ICRA46639.2022.9811752
M3 - Conference contribution
AN - SCOPUS:85136336516
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5184
EP - 5190
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
Y2 - 23 May 2022 through 27 May 2022
ER -