面向协同操纵连续体机器人的静态形状估计的约束感知物理信息神经网络
Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots
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中文总结 AI 辅助
针对协同操纵连续体机器人静态形状估计的闭合链约束挑战，提出约束感知物理信息神经网络，仿真与实验显示其精度高、物理一致性好且计算效率远优于对比方法。
中文摘要 AI 辅助
协同操纵连续体机器人（CCR）的静态形状估计极具挑战性，因为其连续体臂与被操纵的柔性物体形成闭合链，必须同时满足静力学平衡和几何闭环约束。本文提出一种约束感知物理信息神经网络（PINN），用于基于几何变量应变公式建模的腱驱动CCR的静态形状估计。该方法整合了投影静力学平衡残差和构型级几何残差，以强制执行控制力学规律和闭环几何关系。在仿真中，将该PINN与纯数据驱动的人工神经网络（ANN）在有限且含噪声的训练数据下进行对比：当使用140个样本且标签噪声为50%时，PINN将相对构型误差、平衡残差和闭环残差分别降低67.88%、67.35%和88.06%；使用完整数据集时，PINN实现0.1597%的相对构型误差，推理时间为0.1773 ms，而迭代非线性求解器的推理时间为17.97 s。实验微调后，标记点RMSE从2.657 mm降至0.497 mm，R²从-0.788提升至0.937。这些结果表明，该方法可实现闭合链CCR的准确、物理一致且计算高效的静态形状估计。
英文摘要
Static shape estimation of co-manipulative continuum robots (CCRs) is challenging because the continuum arms and manipulated flexible object form a closed chain that must satisfy both static equilibrium and geometric loop-closure constraints. This paper presents a constraint-aware physics-informed neural network (PINN) for static shape estimation of a tendon-driven CCR modeled using the geometric variable strain formulation. The proposed method incorporates a projected static equilibrium residual and a configuration-level geometric residual to enforce the governing mechanics and closed-chain geometry. In simulation, the PINN is compared with a purely data-driven artificial neural network (ANN) under limited and noisy training data. With 140 samples and 50% label noise, the PINN reduces the relative configuration error, equilibrium residual, and closed-chain residual by 67.88%, 67.35%, and 88.06%, respectively. Using the full dataset, the PINN achieves 0.1597% relative configuration error with an inference time of 0.1773 ms, compared with 17.97 s for an iterative nonlinear solver. Experimental fine-tuning reduces the marker RMSE from 2.657 mm to 0.497 mm and increases R2 from -0.788 to 0.937. These results demonstrate accurate, physically consistent, and computationally efficient static shape estimation of closed-chain CCRs.
