张拉整体
人工神经网络
变形(气象学)
人工智能
计算机科学
棱镜
巴(单位)
软机器人
职位(财务)
循环神经网络
计算机视觉
结构工程
工程类
机器人
材料科学
地质学
光学
物理
复合材料
经济
海洋学
财务
作者
Wen-Yung Li,Atsushi Takata,Hiroyuki Nabae,Gen Endo,Koichi Suzumori
出处
期刊:IEEE robotics and automation letters
日期:2021-10-01
卷期号:6 (4): 6228-6234
被引量:12
标识
DOI:10.1109/lra.2021.3091384
摘要
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.
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