发表机构
National University of Singapore; KU Leuven; Khalifa University（新加坡国立大学; 鲁汶大学; 哈利法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对修剪螺旋软臂，提出分离截面本构律与稀疏融合力学，构建动态Cosserat模型，实现高精度快速求解，支持规划与协同设计。
AI 中文摘要
软体机器人的Cosserat杆模型通常通过在一个共同截面上对材料属性求和来构建截面刚度。对于修剪螺旋臂，这一假设变得不准确，因为承重螺旋域是分离的，仅通过稀疏的融合交叉点连接。本文提出了一种分离截面本构律，在每个螺旋域的局部坐标系中评估其本构响应，并将其拉回骨干线，从而得到有效的骨干刚度。稀疏融合力学捕捉了相邻域之间相对运动引起的额外柔度，并确定了弯曲、扭转和拉伸的通道式折减曲线η_c(s/L)。由此产生的有效截面刚度是强各向异性的：弯曲和拉伸约减少一个数量级，而扭转仍接近有效骨干刚度。所得的截面本构律嵌入到一个几何精确的动态Cosserat模型中，采用GVS离散化和路由肌腱驱动。在103个测量配置中，三个数据集给出的合并归一化位置误差分别为7.7%、6.7%和7.8%，而每次全臂求解在一个CPU核心（Intel Xeon, Cascade Lake, 2.8 GHz）上约需0.3秒，从而实现了对架构化软体机器人的快速基于模型的规划、状态和载荷估计以及形态-控制协同设计。
英文摘要
Cosserat rod models for soft robots usually construct sectional stiffness by summing material properties over a common cross-section. This assumption becomes inaccurate for trimmed helicoid arms, where load-bearing helix domains are separated and connected only through sparse fused crossings. This paper formulates a separated-section constitutive law that evaluates each helix domain in its local frame and pulls its constitutive response back to the backbone, yielding an effective backbone stiffness. Sparse-fusion mechanics captures the additional compliance caused by relative motion between neighboring domains and determines channel-wise reduction profiles $η_c(s/L)$ for bending, torsion, and extension. The resulting effective sectional stiffness is strongly anisotropic: bending and extension are reduced by about one order of magnitude, whereas torsion remains close to the effective backbone stiffness. The resulting sectional law is embedded in a geometrically exact dynamic Cosserat model with GVS discretization and routed-tendon actuation. Across 103 measured configurations, the three datasets give pooled normalized position errors of $7.7 \ \%$, $6.7 \ \%$, and $7.8 \ \%$, while each full-arm solve requires approximately $0.3 \ \mathrm{s}$ on one CPU core (Intel Xeon, Cascade Lake, $2.8 \mathrm{GHz}$), enabling rapid model-based planning, state and load estimation, and morphology--control co-design for architected soft robots.
