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

Science China(Technological Sciences) 2024年第5期67卷 1470-1481页

作者： HU Yu TANG Wei QU Yang XU HuXiu KRAMARENKO Yu.Elena ZOU Jun State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou310058China School of Mechanical Engineering Zhejiang UniversityHangzhou310027China Institute of Process Equipment College of Energy EngineeringZhejiang UniversityHangzhou310027China Faculty of Physics Lomonosov Moscow State UniversityMoscow119991Russia Enikolopov Institute of Synthetic Polymeric Materials of Russian Academy of Sciences Moscow117393Russia

Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic *** studies mostly focused on specific sensors and *** is still a lack of generalizable technologies for integrating soft sensing elements into soft actuators and mapping sensor signals to proprioception *** tackle this problem,we employed multi-material 3D printing technology to fabricate sensorized soft-bending actuators(SBAs)using plain and conductive thermoplastic polyurethane(TPU)*** designed various geometric shapes for the sensors and investigated their strain-resistive performance during *** 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 *** sensing and shape prediction steps can run in real-time at a frequency of50 Hz on a consumer-level ***,a method is proposed to enhance the robustness of the learning models using data augmentation to handle unexpected sensor *** the methods are efficient,not only for in-plane 2D shape estimation but also for out-of-plane 3D shape *** 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

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