用高斯参数化方法提升连续体机器人接触力估计精度。
Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs
- 基于因子图构建形状与力联合估计框架，降低未知力的维度。
- 仿真显示在单/多接触场景中，力的位置与大小估计均更准确。
- 支持按需引入基函数，适合复杂受限环境中的逐步力估计。
连续体机器人在非结构化环境中具有优势，但其安全运行依赖于对任意位置外部接触力的精确估计。在未知位置估计多个接触力属于病态问题。本文提出一种统一的形变与受力估计框架，基于因子图建模，将高斯混合力参数化嵌入离散化概率柯西特杆模型，降低外部力的未知维度，缓解节点级力估计的病态性。该框架融合应变、腱张力和姿态测量，同时估计机器人形变与外部力，并考虑建模与传感器不确定性。数值仿真表明，所提方法在单接触与多接触场景下，力的位置与大小估计均优于现有方法。此外，我们提出一种渐进式变体，在模拟受限导航任务中按需引入基函数，实现接触力的顺序估计。
原文摘要 · Abstract (English)
Continuum robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external contact forces acting anywhere along the robot body. Estimating these forces at unknown locations is an ill-conditioned problem, particularly for multiple contacts. We propose a unified shape and force estimation framework formulated on a factor graph. By incorporating a Gaussian mixture force parameterization into a discretized probabilistic Cosserat rod model, we reduce the dimensionality of the unknown external forces and mitigate the ill-conditioning of node-wise force estimation. The framework fuses strain, tendon tension, and pose measurements to simultaneously estimate the robot's shape and external forces while accounting for modeling and sensor uncertainties. Numerical simulations demonstrate that the proposed method outperforms existing methods in terms of force location and magnitude estimation for both single and multi-contact scenarios. We further present a progressive variant that introduces basis functions on demand to estimate contact forces sequentially during a simulated confined-navigation task.
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