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Shape estimation for a TPU-based multi-material 3D printed soft pneumatic actuator using deep learning models
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DOI：10.1007/s11431-023-2619-7.png)
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Abstract
En 中文
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.
Keywords:
shape estimation
soft sensors and actuators
3D printing
deep learning in robotics
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