基于物理模型的连续体机器人状态估计，提升手术场景下形变与外力感知精度。
Continuum Robot State Estimation with Actuation Uncertainty
- 融合机械先验的分段线性应变建模，实现高精度数值计算
- 同时估计形状、外部载荷与驱动输入，误差显著低于传统方法
- 适用于多种构型机器人，适合实时手术导航应用
连续体机器人是柔性细长操作臂，适合在狭小手术空间中使用。由于未知交互力和模型不确定性会显著影响其形变，需通过外部观测进行状态估计。现有方法或忽略驱动建模，或依赖简化的确定性驱动模型。本文提出联合估计机器人形变、外部载荷和驱动输入的方法，采用基于力学原理的驱动先验。为此，我们引入离散柯西杆模型与分段线性应变积分，兼顾高数值精度并生成稀疏因子图结构，支持高效非线性优化。该框架扩展至绳索驱动和平行连续体机器人，在仿真中验证，并在手术用同心管机器人上完成实验验证。整体方法实现了多种机器人架构下的可解释实时估计，且通过线性化因子图直接获取操纵器雅可比矩阵。
原文摘要 · Abstract (English)
Continuum robots are flexible, slender manipulators well suited for confined surgical environments. In these settings, unknown interaction forces and model uncertainty significantly affect robot shape, motivating state estimation from external observations. Existing estimation methods either neglect actuation modeling or rely on simplified deterministic actuation models. In contrast, we jointly estimate robot shape, external loads, and actuation inputs using mechanically principled actuation priors. To achieve this, we present a discrete Cosserat rod formulation with piecewise-linear strain integration that provides high numerical accuracy while inducing a sparse factor graph structure for efficient nonlinear optimization. We extend the framework to tendon-driven and parallel robots in simulation and validate it experimentally on a surgical concentric tube robot. Overall, our approach enables principled real-time estimation across multiple robot architectures while providing direct access to manipulator Jacobians through the linearized factor graph.
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