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

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 robo...