Modeling Fabric-Type Actuator Using Point Clouds by Deep Learning

• Published in: IEEE access Vol. 10; pp. 94363 - 94375
• Main Authors: Peng, Yanhong, Yamaguchi, Hiroki, Funabora, Yuki, Doki, Shinji
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Chamfering; Coders; Computational modeling; Deep learning; Deformation effects; McKibben Artificial muscle; modeling for soft robots; Modelling; Muscles; point cloud; Point cloud compression; Robots; Short term; Simulation; Soft actuators; Soft robotics; Solid modeling; Three dimensional models; Viscoelasticity
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3204652

Flexible actuators are popular in the consumer and medical fields because of their flexibility and compliance. However, they are typically difficult to model because of their viscoelasticity and nonlinearity. This letter proposes a method for correcting the deformation of the simulated flexible robots to make it similar to the deformation of real robots using point clouds by deep learning. Long short-term memory (LSTM) can simulate the next frame of actuator deformation from the previous frames of deformations. In this study, we presented the robots with four different muscle structures. We found that using an encoder-LSTM-decoder network can improve the similarity between the deformation of a learned muscle structure and the real deformation and is also effective in correcting the deformation of the unlearned structures. Our correction method reduced the average Chamfer distance of the simulated point clouds of the basic-type structure actuator from 15.89 to 7.81. This research can provide a new concept for future flexible robot modeling using point clouds.