Modelling complex deformation for soft robotics provides a guideline to understand their behaviour, leading to safe interaction with the environment. However, building a surrogate model with high accuracy and fast inference speed can be challenging for soft robotics due to the nonlinearity from complex geometry, large deformation, material nonlinearity etc. The reality gap from surrogate models also prevents their further deployment in the soft robotics domain. In this study, we proposed a physics-informed Neural Networks (PINNs) named PINN-Ray to model complex deformation for a Fin Ray soft robotic gripper, which embeds the minimum potential energy principle from elastic mechanics and additional high-fidelity experimental data into the loss function of neural network for training. This method is significant in terms of its generalisation to complex geometry and robust to data scarcity as compared to other data-driven neural networks. Furthermore, it has been extensively evaluated to model the deformation of the Fin Ray finger under external actuation. PINN-Ray demonstrates improved accuracy as compared with Finite element modelling (FEM) after applying the data assimilation scheme to treat the sim-to-real gap. Additionally, we introduced our automated framework to design, fabricate soft robotic fingers, and characterise their deformation by visual tracking, which provides a guideline for the fast prototype of soft robotics.

翻译：对软体机器人复杂变形进行建模，为理解其行为提供了指导，从而实现与环境的交互。然而，由于复杂几何结构、大变形、材料非线性等因素带来的非线性特性，为软体机器人构建一个兼具高精度和快速推理速度的代理模型具有挑战性。代理模型存在的现实差距也阻碍了其在软体机器人领域的进一步应用。在本研究中，我们提出了一种名为PINN-Ray的物理信息神经网络（PINNs），用于建模鳍指式软体机器人抓手的复杂变形。该方法将弹性力学中的最小势能原理以及额外的高保真实验数据嵌入神经网络的损失函数中进行训练。与其他数据驱动的神经网络相比，该方法在泛化至复杂几何结构以及对数据稀缺的鲁棒性方面具有重要意义。此外，该方法已通过广泛评估，用于建模鳍指在外部驱动下的变形。在应用数据同化方案处理仿真与现实差距后，PINN-Ray相较于有限元建模（FEM）表现出更高的精度。此外，我们介绍了用于设计、制造软体机器人手指并通过视觉跟踪表征其变形的自动化框架，该框架为软体机器人的快速原型制作提供了指导。