Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs
This work addresses the ill-conditioned problem of multi-contact force estimation for continuum robots, enabling safer operation in unstructured environments.
The paper proposes a unified shape and force estimation framework for continuum robots using Gaussian-parameterized factor graphs, which outperforms existing methods in estimating force location and magnitude for single and multi-contact scenarios.
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.