Shape Recognition of a Tensegrity With Soft Sensor Threads and Artificial Muscles Using a Recurrent Neural Network

• Published in: IEEE robotics and automation letters Vol. 6; no. 4; pp. 6228 - 6234
• Main Authors: Li, Wen-Yung, Takata, Atsushi, Nabae, Hiroyuki, Endo, Gen, Suzumori, Koichi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial muscles; Bars; Deformation; Motion capture; Muscles; Neural networks; Position measurement; Prediction models; Prisms; Recurrent neural networks; Resistance; Robot sensing systems; Rubber; Sensors; Shape; Shape recognition; Soft robot materials and design; soft sensors and actuators; Strain; tensegrity structure; Tensegrity structures; thin McKibben muscle; three-dimensional environment recognition
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3091384

This letter proposes a novel method to accomplish shape recognition by utilizing a tensegrity structure with a soft sensor via a recurrent neural network (RNN). The combination of soft tensegrity and soft sensors make it capable of recognizing the deformation to reflect the shape of its surroundings. As the first step to this goal, we build a three-bar tensegrity prism with nine separate soft sensors in which the resistance value of the sensors changes with length variation. The prism is actuated by thin McKibben muscles and deforms when the pressure inside a muscle. The positions of the six nodes in the prism are obtained using a motion-capture system. The measured resistance and position data are used as training data for the RNN to build a prediction model that can reflect the shape variation during the period of deformation of the tensegrity. If several prisms are connected, they can be used with this approach to recognize the shape of a three-dimensional environment that is difficult to observe directly.