发表机构
University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个基于可微有限元方法的端到端框架，用于气动软体机器人的建模与系统辨识，通过自动网格生成和点云数据校准实现高精度建模，并支持轨迹优化，实验显示最大位置误差小于3毫米。
AI 中文摘要
软体机器人因其非线性、复杂动力学和潜在复杂几何形状而面临显著的建模挑战。这些在精确系统辨识和动力学建模方面的困难限制了它们在精密机器人任务中的应用。先前的建模方法通常在精度、计算效率或速度之间存在权衡。可微有限元方法（FEM）为软体机器人建模提供了这些需求之间的有前景的平衡，并能够利用梯度进行高效校准和轨迹优化。在本文中，我们提出了一种基于可微有限元的端到端框架，旨在简化气动软体机器人的建模和系统辨识，从而为下游任务生成可靠的模型。该框架自动将计算机辅助设计转换为体素化的四面体粘弹性有限元网格，这些网格准确表示机器人的几何形状和驱动机制。通过将易于获取的点云数据与可微有限元方法相结合，该框架利用最小的实验设置实现了材料和动态驱动参数的精确辨识。此外，模型的可微性通过利用来自机器人动力学和接触相互作用的梯度促进了轨迹优化。我们通过建模一个复杂的波纹状气动软体机器人来验证该框架，并展示了其在实际运动规划任务中的有效性。实验结果表明，该框架具有高建模精度，最大位置误差小于3毫米，并在路径跟踪和抓取等任务中成功应用。
英文摘要
Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulties in accurate system identification and dynamics modelling limit their applications in precise robotics tasks. Prior modelling approaches typically suffer from trade-offs in accuracy, computational efficiency, or speed. The differentiable finite element method (FEM) offers a promising balance between these desiderata for soft robot modelling, and enables the use of gradients for efficient calibration and trajectory optimisation. In this paper, we propose an end-to-end differentiable FEM-based framework designed to streamline modelling and system identification for pneumatic soft robots, enabling the generation of reliable models for downstream tasks. The framework automates the conversion of computer-aided designs into voxelised tetrahedral viscoelastic FEM meshes that accurately represent the robot's geometry and actuation mechanism. By integrating easily acquired point cloud data with differentiable FEM, the framework achieves precise identification of material and dynamic actuation parameters using a minimal experimental setup. Additionally, the differentiable nature of the model facilitates trajectory optimisation by leveraging gradients from robot dynamics and contact interactions. We validate the framework by modelling a complex bellow-shaped pneumatic soft robot and demonstrate its efficacy in real-world motion planning tasks. Experimental results indicate high modelling accuracy, with a maximum positional error of less than 3 mm, and successful application in tasks such as path following and grasping.
CommentsPublished in IEEE Robotics and Automation Letters (RA-L)
Journal refIEEE Robotics and Automation Letters, vol. 10, no. 12, pp. 13003-13010, 2025
