Shape estimation for a TPU-based multi-material 3D printed soft pneumatic actuator using deep learning models  

作　　者：HU Yu TANG Wei QU Yang XU HuXiu KRAMARENKO Yu.Elena ZOU Jun 

机构地区：[1]State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou,310058,China [2]School of Mechanical Engineering,Zhejiang University,Hangzhou,310027,China [3]Institute of Process Equipment,College of Energy Engineering,Zhejiang University,Hangzhou,310027,China [4]Faculty of Physics,Lomonosov Moscow State University,Moscow,119991,Russia [5]Enikolopov Institute of Synthetic Polymeric Materials of Russian Academy of Sciences,Moscow,117393,Russia

出　　处：《Science China(Technological Sciences)》2024年第5期1470-1481,共12页中国科学（技术科学英文版）

基　　金：supported by International Cooperation Program of the Natural Science Foundation of China(Grant No.52261135542);Zhejiang Provincial Natural Science Foundation of China(Grant No.LD22E050002);Zhejiang University Global Partnership Fund;grateful to the Russian Science Foundation(Grant No.23-43-00057)for financial support。

摘　　要：Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic compliance.Previous studies mostly focused on specific sensors and actuators.There is still a lack of generalizable technologies for integrating soft sensing elements into soft actuators and mapping sensor signals to proprioception parameters.To tackle this problem,we employed multi-material 3D printing technology to fabricate sensorized soft-bending actuators(SBAs)using plain and conductive thermoplastic polyurethane(TPU)filaments.We designed various geometric shapes for the sensors and investigated their strain-resistive performance during deformation.To address the nonlinear time-variant behavior of the sensors during dynamic modeling,we adopted a data-driven approach using different deep neural networks to learn the relationship between sensor signals and system states.A series of experiments in various actuation scenarios were conducted,and the results demonstrated the effectiveness of this approach.The sensing and shape prediction steps can run in real-time at a frequency of50 Hz on a consumer-level computer.Additionally,a method is proposed to enhance the robustness of the learning models using data augmentation to handle unexpected sensor failures.All the methods are efficient,not only for in-plane 2D shape estimation but also for out-of-plane 3D shape estimation.The aim of this study is to introduce a methodology for the proprioception of soft pneumatic actuators,including manufacturing and sensing modeling,that can be generalized to other soft robots.

关 键 词：shape estimation soft sensors and actuators 3D printing deep learning in robotics 

分 类 号：TH47[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程] TP391.73[自动化与计算机技术—控制科学与工程]