English(EN) Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots

新的PINN方法增强了连续体机器人的静态形状估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种新颖的约束感知物理信息神经网络（PINN），用于精确估计共操作连续体机器人（CCRs）的静态形状。该方法有效地整合了机械平衡和几何闭环约束，在有限和嘈杂数据的情况下，其性能优于纯数据驱动的人工神经网络（ANNs）。PINN在模拟和实验环境中均显示出配置误差、平衡残差和闭链残差的显著降低，实现了高精度和高效率。 AI

影响这项研究可能为医学等领域的机器人系统带来更高的精度和效率。

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在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Rana Danesh, Pari Qarehdaghi, Farrokh Janabi-Sharifi · 2026-08-28 04:00

面向共操作连续体机器人的约束感知物理信息神经网络用于静态形状估计

arXiv:2608.26273v1 Announce Type: cross Abstract: 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 …

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面向共操作连续体机器人的约束感知物理信息神经网络用于静态形状估计

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