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

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

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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 of 50 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.

shape estimationsoft sensors and actuators3D printingdeep learning in robotics

HU Yu、TANG Wei、QU Yang、XU HuXiu、KRAMARENKO Yu.Elena、ZOU Jun

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State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310058,China

School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China

Institute of Process Equipment,College of Energy Engineering,Zhejiang University,Hangzhou 310027,China

Faculty of Physics,Lomonosov Moscow State University,Moscow 119991,Russia

Enikolopov Institute of Synthetic Polymeric Materials of Russian Academy of Sciences,Moscow 117393,Russia

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International Cooperation Program of the Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaZhejiang University Global Partnership FundRussian Science Foundation

52261135542LD22E05000223-43-00057

2024

10.1007/s11431-023-2619-7

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(5)