Physics-informed recurrent neural networks for soft pneumatic actuators
Physics-informed recurrent neural networks for soft pneumatic actuators
复制标题
用于软气动执行器的基于物理的循环神经网络
DOI：
10.1109/lra.2022.3178496
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发表时间：
2022
影响因子：
5.2
通讯作者：
Kohei Nakajima
中科院分区：
文献类型：
--
作者：
Wentao Sun;Nozomi Akashi;Yasuo Kuniyoshi;Kohei Nakajima
用间接传感技术取代传感器有助于保持软机器人的灵活性。通过将物理模型和递归神经网络(我们称之为形而上学信息递归神经网络[PIRNN]方法)相结合，我们实现了两种典型的软气动执行器的混合预测方案：McKibben气动人工肌肉和硅胶气动软指。结果表明，该混合方案在很大程度上提高了预测精度，即使与不准确的物理模型相结合也是如此。我们还介绍了PIRNN方法的广泛适用性，展示了它在不同类型的RNN和软机器人平台上的有效性。我们的工作通过将物理信息机器学习方法应用于软机器人中的实际工程问题来填补文献中的空白。
Replacing sensors with indirect sensing techniques contributes to retaining the flexibility of soft robots. By combining physical models with recurrent neural networks (which we term aphysics-informed recurrent neural network[PIRNN] approach), we implemented a hybrid prediction scheme on two typical soft pneumatic actuators: a McKibben pneumatic artificial muscle and a pneumatic-based soft finger made of silicone. The results showed that this hybrid scheme robustly enhanced the prediction accuracy to a great extent, even when combined with an inaccurate physical model. We also present the broad applicability of the PIRNN approach, showing its effectiveness for diverse types of RNNs and soft robotics platforms. Our work fills the gaps in the literature by applying a physics-informed machine-learning approach to practical engineering problems in soft robotics.